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Journal articles on the topic 'Passengers Flow Estimation'
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Yang, Taoyuan, Peng Zhao, and Xiangming Yao. "A Method to Estimate URT Passenger Spatial-Temporal Trajectory with Smart Card Data and Train Schedules." Sustainability 12, no. 6 (March 24, 2020): 2574. http://dx.doi.org/10.3390/su12062574.
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Precise estimation of passenger spatial-temporal trajectory is the basis for urban rail transit (URT) passenger flow assignment and ticket fare clearing. Inspired by the correlation between passenger tap-in/out time and train schedules, we present a method to estimate URT passenger spatial-temporal trajectory. First, we classify passengers into four types according to the number of their routes and transfers. Subsequently, based on the characteristic that passengers tap-out in batches at each station, the K-means algorithm is used to assign passengers to trains. Then, we acquire passenger access, egress, and transfer time distribution, which are used to give a probability estimation of passenger trajectories. Finally, in a multi-route case of the Beijing Subway, this method presents an estimation result with 91.2% of the passengers choosing the same route in two consecutive days, and the difference of route choice ratio in these two days is 3.8%. Our method has high accuracy and provides a new method for passenger microcosmic behavior research.
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Xie, Mei-Quan, Xia-Miao Li, Wen-Liang Zhou, and Yan-Bing Fu. "Forecasting the Short-Term Passenger Flow on High-Speed Railway with Neural Networks." Computational Intelligence and Neuroscience 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/375487.
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Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.
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Yang, Yuedi, Jun Liu, Pan Shang, Xinyue Xu, and Xuchao Chen. "Dynamic Origin-Destination Matrix Estimation Based on Urban Rail Transit AFC Data: Deep Optimization Framework with Forward Passing and Backpropagation Techniques." Journal of Advanced Transportation 2020 (December 7, 2020): 1–16. http://dx.doi.org/10.1155/2020/8846715.
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At present, the existing dynamic OD estimation methods in an urban rail transit network still need to be improved in the factors of the time-dependent characteristics of the system and the estimation accuracy of the results. This study focuses on predicting the dynamic OD demand for a time of period in the future for an urban rail transit system. We propose a nonlinear programming model to predict the dynamic OD matrix based on historic automatic fare collection (AFC) data. This model assigns the passenger flow to the hierarchical flow network, which can be calibrated by backpropagation of the first-order gradients and reassignment of the passenger flow with the updated weights between different layers. The proposed model can predict the time-varying OD matrix, the number of passengers departing at each time, and the travel time spent by passengers, of which the results are shown in the case study. Finally, the results indicate that the proposed model can effectively obtain a relatively accurate estimation result. The proposed model can integrate more traffic characteristics than traditional methods and provides an effective and hierarchical passenger flow estimation framework. This study can provide a rich set of passenger demand for advanced transit planning and management applications, for instance, passenger flow control, adaptive travel demand management, and real-time train scheduling.
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Su, Guanghui, Bingfeng Si, Kun Zhi, and He Li. "A Calculation Method of Passenger Flow Distribution in Large-Scale Subway Network Based on Passenger–Train Matching Probability." Entropy 24, no. 8 (July 26, 2022): 1026. http://dx.doi.org/10.3390/e24081026.
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The ever-increasing travel demand has brought great challenges to the organization, operation, and management of the subway system. An accurate estimation of passenger flow distribution can help subway operators design corresponding operation plans and strategies scientifically. Although some literature has studied the problem of passenger flow distribution by analyzing the passengers’ path choice behaviors based on AFC (automated fare collection) data, few studies focus on the passenger flow distribution while considering the passenger–train matching probability, which is the key problem of passenger flow distribution. Specifically, the existing methods have not been applied to practical large-scale subway networks due to the computational complexity. To fill this research gap, this paper analyzes the relationship between passenger travel behavior and train operation in the space and time dimension and formulates the passenger–train matching probability by using multi-source data including AFC, train timetables, and network topology. Then, a reverse derivation method, which can reduce the scale of possible train combinations for passengers, is proposed to improve the computational efficiency. Simultaneously, an estimation method of passenger flow distribution is presented based on the passenger–train matching probability. Finally, two sets of experiments, including an accuracy verification experiment based on synthetic data and a comparison experiment based on real data from the Beijing subway, are conducted to verify the effectiveness of the proposed method. The calculation results show that the proposed method has a good accuracy and computational efficiency for a large-scale subway network.
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Nagasaki, Yusaku, Masashi Asuka, and Kiyotoshi Komaya. "A Fast Estimation Method of Railway Passengers' Flow." IEEJ Transactions on Electronics, Information and Systems 126, no. 11 (2006): 1406–13. http://dx.doi.org/10.1541/ieejeiss.126.1406.
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Li, Wei, and Qin Luo. "A data-driven estimation method for potential passenger demand of last trains in metro based on external traffic data." Advances in Mechanical Engineering 11, no. 12 (December 2019): 168781401989835. http://dx.doi.org/10.1177/1687814019898357.
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The last train problem for metro is especially important because the last trains are the last chances for many passengers to travel by metro; otherwise, they have to choose other traffic modes like taxis or buses. Among the problems, the passenger demand is a vital input condition for the optimization of last train transfers. This study proposes a data-driven estimation method for the potential passenger demand of last trains. Through the geographic information, external traffic data including taxi and bus are first analyzed separately to match the origin–destination passenger flow during the last train period. A solving solution for taxi and bus is then developed to estimate the potential passenger flow for all the transfer directions of the target stations. Combining the estimated potential passenger flow and the actual passenger flow obtained by metro smart card data, the total potential passenger demand of last trains is obtained. The effectiveness of the proposed method is evaluated using a real-world metro network. This research can provide important guidance and act as a technical reference for the metro operations on when to optimize the last train transfers.
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Su, Guanghui, Bingfeng Si, Fang Zhao, and He Li. "Data-Driven Method for Passenger Path Choice Inference in Congested Subway Network." Complexity 2022 (February 28, 2022): 1–13. http://dx.doi.org/10.1155/2022/5451017.
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In a congested large-scale subway network, the distribution of passenger flow in space-time dimension is very complex. Accurate estimation of passenger path choice is very important to understand the passenger flow distribution and even improve the operation service level. The availability of automated fare collection (AFC) data, timetable, and network topology data opens up a new opportunity to study this topic based on multisource data. A probability model is proposed in this study to calculate the individual passenger’s path choice with multisource data, in which the impact of the network time-varying state (e.g., path travel time) on passenger path choice is fully considered. First, according to the number and characteristics of OD (origin-destination) candidate paths, the AFC data among special kinds of OD are selected to estimate the distribution of passengers’ walking time and waiting time of each platform. Then, based on the composition of path travel time, its real-time probability distribution is formulated with the distribution of walking time, waiting time, and in-vehicle time as parameters. Finally, a membership function is introduced to evaluate the dependence between passenger’s travel time and the real-time travel time distribution of each candidate path and take the path with the largest membership degree as passenger’s choice. Finally, a case study with Beijing Subway data is applied to verify the effectiveness of the model presented in this study. We have compared and analysed the path calculation results in which the time-varying characteristics of network state are considered or not. The results indicate that a passenger’s path choice behavior is affected by the network time-varying state, and our model can quantify the time-varying state and its impact on passenger path choice.
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Asmael, N. M., and Sh F. Balket. "Demand Estimation of Proposed Bus Rapid Route in Al Kut City." IOP Conference Series: Earth and Environmental Science 961, no. 1 (January 1, 2022): 012026. http://dx.doi.org/10.1088/1755-1315/961/1/012026.
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Abstract Public transit in the city of Al-Kut faces great challenges due to the weakness of the local government abilities in providing adequate conditions for public transport such as wide vehicles, comfortable seats, and other environmentally friendly means of transport that are almost non-use in the city of Kut, where the dependence is heavily on Mini Bus (Kia) and a medium-sized bus, most of which are old, do not operate in an integrated way, compete with each other for the passengers, reduce the flexibility of movement. This study attempts to estimate the demand for the proposed bus rapid route in the city of al Kut as a modern public transport that can contribute to reducing congestion in the city. In this study, the demand for the current public transport network lines in the city was studied, which are 12 lines using boarding / alighting values to determine passenger loads and assess flow on each route in the transportation network using the origin-destination (OD) data from on/off data, then repeat the application on the BRT route, this was done using assignment model in TransCAD software, where the results showed an estimated value for passenger demand on BRT route about 7,616 passengers/hour, which is equivalent to 40.12 % of the transport lines service.
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Cai, Chang-jun, En-jian Yao, Sha-sha Liu, Yong-sheng Zhang, and Jun Liu. "Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit." Discrete Dynamics in Nature and Society 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/136010.
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For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that theρ2values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness.
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Han, Baoming, Weiteng Zhou, Dewei Li, and Haodong Yin. "Dynamic Schedule-Based Assignment Model for Urban Rail Transit Network with Capacity Constraints." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/940815.
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There is a great need for estimation of passenger flow temporal and spatial distribution in urban rail transit network. The literature review indicates that passenger flow assignment models considering capacity constraints with overload delay factor for in-vehicle crowding are limited in schedule-based network. This paper proposes a stochastic user equilibrium model for solving the assignment problem in a schedule-based rail transit network with considering capacity constraint. As splitting the origin-destination demands into the developed schedule expanded network with time-space paths, the model transformed into a dynamic schedule-based assignment model. The stochastic user equilibrium conditions can be equivalent to the equilibrium passenger overload delay with crowding penalty in the transit network. The proposal model can estimate the path choice probability according to the equilibrium condition when passengers minimize their perceptive cost in a schedule-based network. Numerical example in Beijing urban rail transit (BURT) network is used to demonstrate the performance of the model and estimate the passenger flow temporal and spatial distribution more reasonably and dynamically with train capacity constraints.
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Shang, Bin, and Xiao Ning Zhang. "Passengers Flow Forecasting Model of Urban Rail Transit Based on the Macro-Factors." Advanced Engineering Forum 6-7 (September 2012): 688–93. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.688.
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In China, many cities are planning urban rail transit system, but a comprehensive passenger flow estimation model is still lacking. The total passenger flow of urban rail transit in a city depends on many factors, such as urban population, total length of rail lines, gross domestic production of the city etc. To estimate the total passenger flow of urban rail transit, a linear regression model with multiple variables is established in the paper, based on the real data collected in many cities with urban rail transit operating. The comparison of the estimated flow and the real flow in many cities shows that the model is very accurate in passenger flow forecasting.
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Chen, Ting-Zhao, Yan-Yan Chen, and Jian-Hui Lai. "Estimating Bus Cross-Sectional Flow Based on Machine Learning Algorithm Combined with Wi-Fi Probe Technology." Sensors 21, no. 3 (January 27, 2021): 844. http://dx.doi.org/10.3390/s21030844.
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With expansion of city scale, the issue of public transport systems will become prominent. For single-swipe buses, the traditional method of obtaining section passenger flow is to rely on surveillance video identification or manual investigation. This paper adopts a new method: collecting wireless signals from mobile terminals inside and outside the bus by installing six Wi-Fi probes in the bus, and use machine learning algorithms to estimate passenger flow of the bus. Five features of signals were selected, and then the three machine learning algorithms of Random Forest, K-Nearest Neighbor, and Support Vector Machines were used to learn the data laws of signal features. Because the signal strength was affected by the complexity of the environment, a strain function was proposed, which varied with the degree of congestion in the bus. Finally, the error between the average of estimation result and the manual survey was 0.1338. Therefore, the method proposed is suitable for the passenger flow identification of single-swiping buses in small and medium-sized cities, which improves the operational efficiency of buses and reduces the waiting pressure of passengers during the morning and evening rush hours in the future.
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Pavlyuk, Dmitry, Nadežda Spiridovska, and Irina Yatskiv (Jackiva). "SPATIOTEMPORAL DYNAMICS OF PUBLIC TRANSPORT DEMAND: A CASE STUDY OF RIGA." Transport 35, no. 6 (January 6, 2021): 576–87. http://dx.doi.org/10.3846/transport.2020.14159.
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Sustainable urban mobility remains an emerging research topic during last decades. In recent years, the smart card data collection systems have become widespread and many studies have been focused on usage of anonymized data from these systems for better understanding of mobility patterns of Public Transport (PT) passengers. Data-driven mobility patterns can benefit transport planners at strategic, tactical, and operational levels. A particular point of interest is a spatiotemporal dynamics of mobility patterns that highlights transformation of the PT passenger flows over the time continuously or in response to modifications of the PT system and policies. This study is aimed to estimation and analysis of the spatiotemporal dynamics of PT passenger flows in Riga (Latvia). A multi-stage methodology was proposed and includes three main stages: (1) estimation of individual trip vectors, (2) clustering of trip vectors into spatiotemporal mobility patterns, and (3) further analysis of mobility patterns’ dynamics. The best practice methods are applied at every stage of the proposed methodology: the smart card validation flow is used for extracting information on boarding locations; the trip chain approach is used for estimation of individual trip destinations; vector-based clustering algorithms are utilised for identification of mobility patterns and discovering their dynamics. The resulting methodology provides an advanced tool for observing and managing of PT demand fluctuation on a daily basis. The methodology was applied for mining of a large smart card data set (124 million records) for year 2018. Most important empirical results include obtained daily mobility patterns in Riga, their clusters, and within-cluster dynamics over the year. Obtained daily mobility patterns allows estimation of a city-level PT origin–destination matrix that is useful in many applied areas, e.g., dynamic passenger flow assignment models. Mobility pattern-based clustering of days allows effective comparison and flexible tuning of the PT system for different days of a week, public holidays, extreme weather conditions, and large events. Dynamics of mobility patterns allows estimating the effect of implementing changes (e.g., fare increase or road maintenance) and demand forecasting for user-focused development of PT system.
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Zhang, Jun, Jiaze Liu, and Zhizhong Wang. "Convolutional Neural Network for Crowd Counting on Metro Platforms." Symmetry 13, no. 4 (April 17, 2021): 703. http://dx.doi.org/10.3390/sym13040703.
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Owing to the increased use of urban rail transit, the flow of passengers on metro platforms tends to increase sharply during peak periods. Monitoring passenger flow in such areas is important for security-related reasons. In this paper, in order to solve the problem of metro platform passenger flow detection, we propose a CNN (convolutional neural network)-based network called the MP (metro platform)-CNN to accurately count people on metro platforms. The proposed method is composed of three major components: a group of convolutional neural networks is used on the front end to extract image features, a multiscale feature extraction module is used to enhance multiscale features, and transposed convolution is used for upsampling to generate a high-quality density map. Currently, existing crowd-counting datasets do not adequately cover all of the challenging situations considered in this study. Therefore, we collected images from surveillance videos of a metro platform to form a dataset containing 627 images, with 9243 annotated heads. The results of the extensive experiments showed that our method performed well on the self-built dataset and the estimation error was minimum. Moreover, the proposed method could compete with other methods on four standard crowd-counting datasets.
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Hu, Xing Hua, and Yu Zhang. "The Application of Fluid Analogy Method for Estimating Transit Route ODs Using IC Card On-Off Passenger Data." Applied Mechanics and Materials 694 (November 2014): 73–79. http://dx.doi.org/10.4028/www.scientific.net/amm.694.73.
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Transit route ODs are important reference data for transit network planning and operation management. However, it has the difficulties in data collection, and is of high expense and survey error by means of manual investigation method. It is easy to collect the passenger boarding-alighting counts. The method of fluid analogy method (FAM) is presented for transit passenger OD estimation. It uses the concept of pipe flow, transit route and passengers are regarded as the pipe and fluid, generate bus route OD matrix based upon passenger boarding and alighting counts. The accuracy of route OD estimates generated from boarding-alighting data is tested against actual transit integrated circuit cards (IC cards) OD based on Beijing transit route. It is shown that error index for OD at AM peak hours, PM peak hours and all day is 0.79, 0.80 and 0.83, the result shows that it has a good performance. While the correlation coefficient for actual traveled stops and estimated at AM peak hours, PM peak hours and all day is 0.98, 0.98 and 0.99, which further validate the accuracy and reliability of FAM method in estimating transit route ODs. Compared with traditional equalization algorithms and other analytical model, the method is simple, efficient and able to get a unique solution OD. It has a high practical value in real-time scheduling of intelligent public transport.
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Zhou, Feng, Jun-gang Shi, and Rui-hua Xu. "Estimation Method of Path-Selecting Proportion for Urban Rail Transit Based on AFC Data." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/350397.
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With the successful application of automatic fare collection (AFC) system in urban rail transit (URT), the information of passengers’ travel time is recorded, which provides the possibility to analyze passengers’ path-selecting by AFC data. In this paper, the distribution characteristics of the components of travel time were analyzed, and an estimation method of path-selecting proportion was proposed. This method made use of single path ODs’ travel time data from AFC system to estimate the distribution parameters of the components of travel time, mainly including entry walking time (ewt), exit walking time (exwt), and transfer walking time (twt). Then, for multipath ODs, the distribution of each path’s travel time could be calculated under the condition of its components’ distributions known. After that, each path’s path-selecting proportion can be estimated. Finally, simulation experiments were designed to verify the estimation method, and the results show that the error rate is less than 2%. Compared with the traditional models of flow assignment, the estimation method can reduce the cost of artificial survey significantly and provide a new way to calculate the path-selecting proportion for URT.
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Conchillo, Ángela, Miguel Ángel Recarte, Luis Nunes, and Trinidad Ruiz. "Comparing Speed Estimations from a Moving Vehicle in Different Traffic Scenarios: Absence versus Presence of Traffic Flow." Spanish Journal of Psychology 9, no. 1 (May 2006): 32–37. http://dx.doi.org/10.1017/s1138741600005941.
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The aim of this research was to study the performance in a speed estimation task of a passenger travelling in a real car in different scenarios: a closed track used in previous experimental studies was compared with interurban traffic environment involving a secondary road and a highway. At the same time, the effect of sex and driving experience on speed estimation was analyzed. Thirty-six participants (18 male and 18 female, half of each group being drivers and half non-drivers) estimated the speed of the car in which they travelled as passengers. The actual speed values varied in the range of 40-100 km/h for the secondary road, 70-120 km/h for the highway condition, and 40-120 km/h for the track. The results obtained for the track in previous studies (Recarte & Nunes, 1996; Recarte, Conchillo, & Nunes, 2004, 2005) were replicated in the same condition and were also verified for the secondary road scenario. However, a different pattern of errors was found for the highway. From the viewpoint of psychophysics, the participants were more accurate on the without-traffic track than in real traffic conditions, considered as a whole. The differences found between road and highway are discussed. No effect was found for between- subject variables, sex, and driving experience.
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Ma, Xiaoyu, Wei Xi, Zuhao Chen, Han Hao, and Jizhong Zhao. "ECC: Passenger Counting in the Elevator Using Commodity WiFi." Applied Sciences 12, no. 14 (July 21, 2022): 7321. http://dx.doi.org/10.3390/app12147321.
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Elevators have become a kind of indispensable facility for everyday life, which bring people both convenience and safety hazards. Specifically in the household environment, an elevator’s lifespan is expected to be more than 20 years. An appropriate and regularly maintained counterweight is conducive to extending elevator life. This paper proposes a passenger counting approach in the elevator for regular counterweight adjustment based on commodity WiFi called ECC. Since the running time of the elevator between two adjacent floors is short, the major challenge of ECC is how to count passengers from the limited captured data. This paper first theoretically analyzes the relationship between the number of passengers and the variation of channel state information (CSI). Then ECC constructs a multi-dimensional feature by extracting the average of amplitude (AOA), time-varying spectrum (TVS), and percentage of non-zero elements (PEM) features from the limited data. Finally, the random forest (RF) classifier is used for passenger counting and the local optimization problem is solved by expanding the feature dataset through data segmentation. ECC is implemented by using off-the-shelf IEEE 802.11n devices, and its performance is evaluated via extensive experiments in typical real-world scenes. The estimated precision of ECC can reach more than 95%, and more than 97% of estimation errors are less than 2 persons, which demonstrates the superior effectiveness and generalizability of ECC.
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Sarsam, Saad Issa. "ASSESSING THE RISK AND POTENTIAL OF PERSONAL EXPOSURE TO ROAD GENERATED POLLUTANT EMISSIONS THROUGH URBAN TRANSPORTATION SYSTEM." Journal of Engineering 14, no. 01 (March 1, 2008): 2111–17. http://dx.doi.org/10.31026/j.eng.2008.01.05.
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This paper presents a study to assess the degree of personal exposure to traffic generated pollutant emission along urban arterials in Mosul. The traffic flow characteristics (volume, speed, density, and vehicle type) were determined in the field at selected locations on the arterials.The vehicular traffic which includes (drivers, number of passengers in vehicles on the road, and pedestrian) exposed to road generated emissions were obtained through field survey.The vehicle emissions of CO, VOC, and NOx were calculated using air pollution estimation computer model (Mobile 4.1). It was concluded that the emission of CO, VOC, and NOx exceeds the standard level requirements. The risk arising from personal exposure to traffic generated emissions of such pollutants was analyzed and the degree of personal exposure of road users (drivers, passengers, and pedestrians) to pollutants emission along urban arterials in Mosul was determined.
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Zhu, Wei, Feng Zhou, Jiajun Huang, and Ruihua Xu. "Validating Rail Transit Assignment Models with Cluster Analysis and Automatic Fare Collection Data." Transportation Research Record: Journal of the Transportation Research Board 2526, no. 1 (January 2015): 10–18. http://dx.doi.org/10.3141/2526-02.
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Passenger flow data are necessary for making and coordinating operational plans for urban rail transit (URT) systems; the availability and the service state of those systems directly influence the activity of a city and its people. Although many transit assignment models have been developed, the results of passenger flows estimated by these models as well as assumptions made in the estimation process, especially for large-scale, complex, and dynamically changing URT networks, had not been validated. This paper proposes a methodology that can validate existing URT assignment models by using automatic fare collection data and a cluster analysis technique. Initial applications to the URT system of Shanghai, China, which is one of the largest in the world, show that the proposed approach works well and can efficiently find the origin–destination pairs in which passengers' route choices are misestimated by those assignment models. The analysis suggests that several factors result in errors (for the URT assignment model used in Shanghai). These factors include the threshold for the difference in travel costs, a misrepresentation of the transferring cost, and inadequate values for the standard deviation. This information is useful for detecting errors in existing URT assignment models, leading to improvements.
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Montero-Lamas, Yaiza, Margarita Novales, Alfonso Orro, and Graham Currie. "A New Big Data Approach to Understanding General Traffic Impacts on Bus Passenger Delays." Journal of Advanced Transportation 2023 (May 11, 2023): 1–15. http://dx.doi.org/10.1155/2023/4082587.
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This paper presents a new method to quantify the potential user time savings if the urban bus is given preferential treatment, changing from mixed traffic to an exclusive bus lane, using a big data approach. The main advantage of the proposal is the use of the high amount of information that is automatically collected by sensors and management systems in many different situations with a high degree of spatial and temporal detail. These data allow ready adjustment of calculations to the specific reality measured in each case. In this way, we propose a novel methodology of general application to estimate the potential passenger savings instead of using simulation or analytical methods already present in the literature. For that purpose, in the first place, a travel time prediction model per vehicle trip has been developed. It has been calibrated and validated with a historical series of observations in real-world situations. This model is based on multiple linear regression. The estimated bus delay is obtained by comparing the estimated bus travel time with the bus travel time under free-flow conditions. Finally, estimated bus passenger time savings would be obtained if an exclusive bus lane had been implemented. An estimation of the passenger’s route in each vehicle trip is considered to avoid average value simplifications in this calculation. A case study is conducted in A Coruña, Spain, to prove the methodology's applicability. The results showed that 18.7% of the analyzed bus trips underwent a delay exceeding 3 min in a 2,448 m long corridor, and more than 33,000 h per year could have been saved with an exclusive bus lane. Understanding the impact of different factors on transit and the benefits of a priority bus system on passengers can help city councils and transit agencies to know which investments to prioritize given their limited budget.
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Castillo-Calderón, Jairo, Rubén Carrión Jaura, Diego Díaz Sinche, and Bryan Panchana. "Estimation of Traction Energy Consumption of Urban Service Buses in an Intermediate Andean City." IOP Conference Series: Earth and Environmental Science 1141, no. 1 (February 1, 2023): 012001. http://dx.doi.org/10.1088/1755-1315/1141/1/012001.
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Abstract The purpose of this work is to estimate the traction energy consumption of public transport buses in the urban sector of the city of Loja, Ecuador. Initially, with a data logger device, connected to the OBDII port, the speed and position variables of the transport units are acquired in real time, at a frequency of 1 Hz, during 25 round trips on 3 bus lines with the highest passenger flow; the effects of the slope profile are considered. To avoid information bias, 25 different HINO AK bus units, with different drivers, are monitored on full daily working days, where traffic is variable; different mass of the bus is defined in the three lines, based on historical data of the average number of passengers. Then, based on the fundamental theory of vehicle dynamics, the traction energy consumption of the buses is obtained in Matlab Simulink. For this purpose, a typical driving cycle (TDC) is de-fined, through a deterministic method, for both the outbound and the return trip of each line. The results highlight a higher traction energy consumption on line L8, with 50.36 kWh, where 52.43% is associated with slope resistance, 35.76% inertia resistance, 10.06% rolling resistance and 1.75% aerodynamic resistance. These findings represent a starting point for subsequent studies, in this Andean city, related to electromobility in mass transportation systems.
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Lu, Kai, Alireza Khani, and Baoming Han. "A Trip Purpose-Based Data-Driven Alighting Station Choice Model Using Transit Smart Card Data." Complexity 2018 (August 28, 2018): 1–14. http://dx.doi.org/10.1155/2018/3412070.
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Automatic fare collection (AFC) systems have been widely used all around the world which record rich data resources for researchers mining the passenger behavior and operation estimation. However, most transit systems are open systems for which only boarding information is recorded but the alighting information is missing. Because of the lack of trip information, validation of utility functions for passenger choices is difficult. To fill the research gaps, this study uses the AFC data from Beijing metro, which is a closed system and records both boarding information and alighting information. To estimate a more reasonable utility function for choice modeling, the study uses the trip chaining method to infer the actual destination of the trip. Based on the land use and passenger flow pattern, applying k-means clustering method, stations are classified into 7 categories. A trip purpose labelling process was proposed considering the station category, trip time, trip sequence, and alighting station frequency during five weekdays. We apply multinomial logit models as well as mixed logit models with independent and correlated normally distributed random coefficients to infer passengers’ preferences for ticket fare, walking time, and in-vehicle time towards their alighting station choice based on different trip purposes. The results find that time is a combined key factor while the ticket price based on distance is not significant. The estimated alighting stations are validated with real choices from a separate sample to illustrate the accuracy of the station choice models.
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Zhu, Wei, Wei Wang, and Zhaodong Huang. "Estimating Train Choices of Rail Transit Passengers with Real Timetable and Automatic Fare Collection Data." Journal of Advanced Transportation 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/5824051.
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An urban rail transit (URT) system is operated according to relatively punctual schedule, which is one of the most important constraints for a URT passenger’s travel. Thus, it is the key to estimate passengers’ train choices based on which passenger route choices as well as flow distribution on the URT network can be deduced. In this paper we propose a methodology that can estimate individual passenger’s train choices with real timetable and automatic fare collection (AFC) data. First, we formulate the addressed problem using Manski’s paradigm on modelling choice. Then, an integrated framework for estimating individual passenger’s train choices is developed through a data-driven approach. The approach links each passenger trip to the most feasible train itinerary. Initial case study on Shanghai metro shows that the proposed approach works well and can be further used for deducing other important operational indicators like route choices, passenger flows on section, load factor of train, and so forth.
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Nithya, D. S., Giuseppe Quaranta, Vincenzo Muscarello, and Man Liang. "Review of Wind Flow Modelling in Urban Environments to Support the Development of Urban Air Mobility." Drones 8, no. 4 (April 9, 2024): 147. http://dx.doi.org/10.3390/drones8040147.
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Urban air mobility (UAM) is a transformative mode of air transportation system technology that is targeted to carry passengers and goods in and around urban areas using electric vertical take-off and landing (eVTOL) aircraft. UAM operations are intended to be conducted in low altitudes where microscale turbulent wind flow conditions are prevalent. This introduces flight testing, certification, and operational complexities. To tackle these issues, the UAM industry, aviation authorities, and research communities across the world have provided prescriptive ways, such as the implementation of dynamic weather corridors for safe operation, classification of atmospheric disturbance levels for certification, etc., within the proposed concepts of operation (ConOps), certification standards, and guidelines. However, a notable hindrance to the efficacy of these solutions lies in the scarcity of operational UAM and observational wind data in urban environments. One way to address this deficiency in data is via microscale wind modelling, which has been long established in the context of studying atmospheric dynamics, weather forecasting, turbine blade load estimation, etc. Thus, this paper aims to provide a critical literature review of a variety of wind flow estimation and forecasting techniques that can be and have been utilized by the UAM community. Furthermore, a compare-and-contrast study of the commonly used wind flow models employed within the wind engineering and atmospheric science domain is furnished along with an overview of the urban wind flow conditions.
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Prakash, Ashwini Bukanakere, Ranganathaiah Sumathi, and Honnudike Satyanarayana Sudhira. "Hybrid travel time estimation model for public transit buses using limited datasets." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 4 (December 1, 2023): 1755. http://dx.doi.org/10.11591/ijai.v12.i4.pp1755-1764.
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<p>A reliable transit service can motivate commuters to switch their traveling<br />mode from private to public. Providing necessary information to passengers<br />will reduce the uncertainties encountered during their travel and improve<br />service reliability. This article addresses the challenge of predicting dynamic<br />travel times in urban areas where real-time traffic flow information is<br />unavailable. In this perspective, a hybrid travel time estimation model<br />(HTTEM) is proposed to predict the dynamic travel time using the predicted<br />travel times of the machine learning model and the preceding trip details. The<br />proposed model is validated using the location data of public transit buses of,<br />Tumakuru, India. From the numerical results through error metrics, it is found<br />that HTTEM improves the prediction accuracy, finally, it is concluded that the<br />proposed model is suitable for estimating travel time in urban areas with<br />heterogeneous traffic and limited traffic infrastructure.</p>
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Zhang, Yi, Jianhua Zhang, Baihong Tan, Shuxian He, Liqun Peng, and Tony Z. Qiu. "An Occupancy-Based Adaptive Signal Control for a Congested Signalized Intersection in the Low CV Penetration Environment." Journal of Advanced Transportation 2022 (May 14, 2022): 1–18. http://dx.doi.org/10.1155/2022/4745879.
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Adaptive signal control (ASC) is a well-researched topic that offers an efficient way for traffic management. It possesses a powerful ability to accommodate complex and constantly changing urban transportation networks. With the development of vehicular communication, CV-based ASC shows remarkable advantages compared with the traditional ASC system. Though the existing CV-based ASC strategies were proposed in the past few years, however, there are still issues to overcome. Most of the studies on CV-based ASC are based on the assumption of high CV penetration rate, which often result in poor performance when applied to low CV penetration environments. Besides, the lack of consideration for mixed traffic flow, which is in terms of both the vehicle types and CV penetration of different types of vehicles. To solve these issues, this paper developed an Occupancy-Based ASC strategy for a congested signalized intersection to optimize signal timing and reduce total passenger delay in the low CV penetration environment. Focused on the issues existing in the low CV penetration environment, a Maximum Likelihood Estimation (MLE) model was proposed to estimate vehicle arrivals, and two traffic models, MicroDM and MacroDM, were developed to model the mixed traffic flow and estimate passenger delay. With the purpose of offering fair treatment to passengers approaching the intersection, we proposed an Occupancy-Based Adaptive Signal Control strategy. By transforming the complex signal control problem into a mixed-integer linear programming problem, we found the optimal solution for minimizing total passenger delay. We then evaluated the proposed Occupancy-Based ASC strategy using simulation case studies. The results show that changing traffic status could be captured and estimated with the real-time CV trajectory data as input. Applying the Occupancy-Based ASC control strategy, phases with HOVs or more vehicles will be allocated more travel time. In particular, optimization results show that the proposed Occupancy-Based ASC strategy effectively balances passenger travel demands during peak volume periods.
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Leurent, Fabien, and Kang Liang. "How Do Individual Walk Lengths and Speeds, Together with Alighting Flow, Determine the Platform Egress Times of Train Users?" Journal of Advanced Transportation 2022 (July 19, 2022): 1–22. http://dx.doi.org/10.1155/2022/3633293.
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Egress times of railway passengers from train alighting up to station exit typically amount to some tens of seconds, but with much variability even at the train level. Here, we first model the egress time as the ratio of the walk length to the preferred walk speed, under free-flow conditions. Then, we model the possible occurrence of congestion among the users alighting from a train as a traffic bottleneck affecting those passing at a “queue focal point” during a “queued time interval.” Analytical formulas are provided for the CDF and PDF of egress times, covering the free-flow case and the congested case. Their computation is straightforward for bivariate Gaussian length-speed walk pair. A maximum-likelihood method is developed, together with a quick estimation procedure. A case study of four contrasted trains serving an urban mass transit station in Paris is reported. One train experienced free-flow alighting conditions, whereas each of the other three had its own bottleneck. The MLE method enabled us to recover all parameters but one, due to an issue of identifiability: the solution was to take the mean walk speed as exogenous.
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Yang, Fei, Lin Chen, Yang Cheng, Xia Luo, and Bin Ran. "An Empirical Study of Parameter Estimation for Stated Preference Experimental Design." Mathematical Problems in Engineering 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/292608.
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The stated preference experimental design can affect the reliability of the parameters estimation in discrete choice model. Some scholars have proposed some new experimental designs, such as D-efficient, Bayesian D-efficient. But insufficient empirical research has been conducted on the effectiveness of these new designs and there has been little comparative analysis of the new designs against the traditional designs. In this paper, a new metro connecting Chengdu and its satellite cities is taken as the research subject to demonstrate the validity of the D-efficient and Bayesian D-efficient design. Comparisons between these new designs and orthogonal design were made by the fit of model and standard deviation of parameters estimation; then the best model result is obtained to analyze the travel choice behavior. The results indicate that Bayesian D-efficient design works better than D-efficient design. Some of the variables can affect significantly the choice behavior of people, including the waiting time and arrival time. The D-efficient and Bayesian D-efficient design for MNL can acquire reliability result in ML model, but the ML model cannot develop the theory advantages of these two designs. Finally, the metro can handle over 40% passengers flow if the metro will be operated in the future.
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Cheng, Yan, Xiafei Ye, and Taku Fujiyama. "Identifying Crowding Impact on Departure Time Choice of Commuters in Urban Rail Transit." Journal of Advanced Transportation 2020 (June 23, 2020): 1–16. http://dx.doi.org/10.1155/2020/8850565.
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Crowding in urban rail transit is an inevitable issue for most of the high-density cities across the world, especially during peak time. For commuters who have considerably fixed destination arrival times, departure time choice is an important tool to adjust their trips. The ignorance of crowding impact on commuters’ departure time choice in urban rail transit may cause errors in forecasting dynamic passenger flow during peak time in urban rail transit. The paper develops a mixed logit model to identify how crowding impacts the departure time choice of commuters and their taste variation. Arrival time value was firstly measured in a submodel by applying the reference point approach and then integrated to the main model. Considering the characteristics of human perception, we divided crowding into five grades with distinct circumstances. All parameter distributions were assumed based on their empirical distributions revealed through resampling. The data from Shanghai Metro used for estimation were collected by a specifically designed survey, which combines revealed preference questions and stated preference experiments to investigate the willingness and extent of changing departure time choice of passengers who experienced various grades and duration of crowding in the most crowded part. The result shows that an asymmetric valuation model with preferred arrival time as the only reference point best captured commuters’ responses to arrival time. The departure time choice model clearly identified that only crowding ranging from Grades 3 to 5 had an impact on commuters’ departure time choice. The parameters of crowding costs can be assumed to follow transformed lognormal distributions. It is found that the higher the grade of crowding is, the bigger the impact each unit of crowding cost has on commuters’ departure time choice, while commuters’ tastes get more concentrated when crowded situation upgrades. The model in this paper can help policymakers better understand the interaction between commuters’ departure time choice and crowding alleviation.
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Utku, Anıl, and Sema Kayapinar Kaya. "Multi-layer perceptron based transfer passenger flow prediction in Istanbul transportation system." Decision Making: Applications in Management and Engineering 5, no. 1 (March 20, 2022): 208–24. http://dx.doi.org/10.31181/dmame0315052022u.
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Estimating passenger movement in transportation networks is a critical aspect of public transportation systems. It allows for a greater understanding of traffic patterns, as well as efficient system evaluation and monitoring. It could also help with precise timing to emergencies or important events, as well as the improvement of urban transport system weaknesses and service quality. The number of transfer passengers demand in Istanbul, Turkey's biggest and most developed metropolis, was used to construct a real-world forecasting model in this study. The number of transfer passengers has been forecasted using popular machine learning methods such as kNN (k-Nearest Neighbours), LR (Linear Regression), RF (Random Forest), SVM (Support Vector Machine), XGBoost and MLP. The dataset utilized is made up of hourly passenger transfer counts gathered at two public transportation transfer stations in Istanbul in January 2020. Using MSE, RMSE, MAE and R2 parameters, each model's experimental data have been thoroughly evaluated. MLP has more successfully other machine learning algorithms in the majority of transportation lines, according to the experimental results.
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Ozerova, Olga, Petro Yanovsky, Viktoriia Yanovska, Sergiy Lytvynenko, Larysa Lytvynenko, and Serhii Martseniuk. "Estimation of the interaction level between urban passenger transport and city train." MATEC Web of Conferences 294 (2019): 04008. http://dx.doi.org/10.1051/matecconf/201929404008.
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In the article the estimation of the interaction level between urban passenger transport and city train was made using the systems approach through application of modern methods of developing adequate easy-to-use mathematical models. Applying the systems approach, the transport node was considered as a comprehensive object, which is a single entity. It was identified that the transport node efficiency depends on the interaction level of the structure and its technology with the passenger traffic that requires designing a rational structure of the node and providing the technological interaction setting. The flow chart of determination the indicators of urban passenger transport modes’ operation was proposed in order to improve the passenger service quality by increasing the level of interaction of urban passenger transport modes with each other, also the system of efficient use of urban transport means was developed. The system of criteria for assessing the level of interaction between urban passenger transport and city train was implemented, which consists of criteria for the individual assessment of urban passenger transport and city train and criteria for their system interoperability, including consideration of the system quality from the passenger’s point of view.
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He, Bing, Kang Liu, Zhe Xue, Jiajun Liu, Diping Yuan, Jiyao Yin, and Guohua Wu. "Spatial and Temporal Characteristics of Urban Tourism Travel by Taxi—A Case Study of Shenzhen." ISPRS International Journal of Geo-Information 10, no. 7 (June 30, 2021): 445. http://dx.doi.org/10.3390/ijgi10070445.
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Tourism networks are an important research part of tourism geography. Despite the significance of transportation in shaping tourism networks, current studies have mainly focused on the “daily behavior” of urban travel at the expense of tourism travel, which has been regarded as an “exceptional behavior”. To fill this gap, this study proposes a framework for exploring the spatial and temporal characteristics of urban tourism travel by taxi. We chose Shenzhen, a densely populated mega-city in China with abundant tourism resources, as a case study. First, we extracted tourist trips from taxi trajectories and used kernel density estimation to analyze the spatial aggregation characteristics of tourist trip origins. Second, we investigated the spatial dependence of tourist trips using local spatial autocorrelation analysis (Getis-Ord Gi*). Third, we explored the correlations between the tourist trip origins and urban geographic contextual factors (e.g., catering services and transportation facilities) using a geographically weighted regression model. The results show the following: (1) the trends between the coverage of tourist travel networks and the volume of tourist trips are similar; (2) the spatial interaction intensity of urban tourism has grouping and hierarchical characteristics; and (3) the spatial distribution of tourist trips by taxi is uneven and influenced by the distribution of urban morphology, tourism resources, and the preferences of taxi pick-up passengers. Our proposed framework and revealed spatial and temporal patterns have implications for urban tourism traffic planning, tourism product development, and tourist flow control in tourist attractions.
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Yin, Haodong, Jianjun Wu, Huijun Sun, Yunchao Qu, Xin Yang, and Bo Wang. "Optimal Bus-Bridging Service under a Metro Station Disruption." Journal of Advanced Transportation 2018 (July 5, 2018): 1–16. http://dx.doi.org/10.1155/2018/2758652.
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A station disruption is an abnormal operational situation that the entrance or exit gates of a metro station have to be closed for a certain of time due to an unexpected incident. The passengers’ travel behavioral responses to the alternative station disruption scenarios and the corresponding controlling strategies are complex and hard to capture. This can lead to the hardness of estimating the changes of the network-wide passenger demand, which is the basis of carrying out a response plan. This paper will establish a model to solve the metro station disruption problem by providing optimal additional bus-bridging services. Two main contributions are made: (1) a three-layer discrete choice behavior model is developed to analyze the dynamic passenger flow demand under station disruption; and (2) an integrated algorithm is designed to manage and control the station disruption crisis by providing additional bus-bridging services with the objective of minimizing the total travel time of affected passengers and the operating cost of bridging-buses. Besides, the multimodal transport modes, including metro, bridging-bus, shared-bike, and taxi, are considered as passengers’ alternative choices in face of the station disruption. A numerical study based on the Beijing metro network shows that additional bus-bridging services can significantly eliminate the negative impact of the station disruption.
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Skirkouski, Siarhei, Uladzimir Sedziukevich, and Olha Svichynska. "JUSTIFICATION OF THE CHOICE OF PUBLIC TRANSPORT SERVICE TYPE ON THE ROUTE." Automobile transport, no. 48 (May 29, 2021): 79–85. http://dx.doi.org/10.30977/at.2219-8342.2021.48.0.79.
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Problem. Currently, there exist two main types of service on public transport routes – headway-based and timetable-based. They differ by the frequency of service at the stops and by the information available for passengers. The required frequency of service significantly affects transport operator costs and passenger travel time which, in turn, influences the cost for a passenger. One of the ways to reduce costs for both parties of the transportation process is to make a reasonable choice of the type of service or switch between the types during the day depending on the passenger flow volume. Goal. In the paper, to justify the choice of between the types of service, the cost of public transport vehicles operation and the losses of passenger travel times are taken into account. Methodology. The developed way of making a choice about the type of service allows increasing the efficiency of urban public transport due to reducing the passenger waiting time by switching from the headway-based service in rush hours to timetable-based service in the periods of passenger flow volume decline. Results. The relationship allowing making a decision about the type of service on the route is developed. It allows to correctly introduce the type of service which will ensure the balance between the transport operator and passenger costs. Originality. The developed approach to make a decision on the type of service allows reducing the passenger waiting time by switching from headway-based service in rush hours to the timetable-based service in the periods of decline in passenger flow volume during the day. Practical value. The results of the research based on the survey data allowed estimating the numerical value of the headway at which it is expedient to switch to the timetable-based service.
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Zhao, Xueke. "Proportional Estimation Method of Urban Rail Passenger Flow Transfer Path Selection Based on IC Card Data." Academic Journal of Science and Technology 5, no. 2 (March 19, 2023): 21–26. http://dx.doi.org/10.54097/ajst.v5i2.5927.
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Based on the analysis of the influencing factors of urban rail transit passenger flow transfer path selection behavior and the components of travel time, a method of estimating the proportion of urban rail transit passenger flow transfer path selection based on IC card data is proposed. Firstly, the research determines the shortest interchange path algorithm as Dijkstra algorithm and the graph-based depth-first search algorithm as the effective interchange path search algorithm; analyzes the factors influencing the passenger flow interchange path selection behavior as three types of travel time, interchange cost and road network familiarity; constructs the generalized cost function of effective interchange path selection by combining travel time and interchange cost; establishes the passenger flow probability selection model by combining road network familiarity. To eliminate the influence of absolute difference of utility on the probability of path selection in the polynomial logit model, the path probability selection model is improved with the help of minimum travel cost; Bayesian estimation is used to compare the passenger travel time with the theoretical travel time to identify the closest effective interchange path, and the joint distribution probability is used to calibrate the probability selection model in terms of travel time and interchange cost. The coefficient, coefficient of interchange passenger flow, penalty coefficient of interchange number, familiarity of road network and other related parameters are combined with the joint distribution probability to calibrate the probability of selection of different effective interchange paths.
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Zhang, Jianming, Jun Cai, Mengjia Wang, and Wansong Zhang. "An Estimation Method for Passenger Flow Volumes from and to Bus Stops Based on Land Use Elements: An Experimental Study." Land 13, no. 7 (July 2, 2024): 971. http://dx.doi.org/10.3390/land13070971.
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To unravel the general relationship between bus travel and land use around bus stops and along bus routes and to promote their coordinated development, this paper explores a method to estimate passenger flow volumes from and to bus stops based on land use types, intensities, and spatial distributions around bus stops and along bus routes. Firstly, following the principle of the gravity model, which considers traffic volumes analogous to gravity based on trip generation and distance impedance between traffic analysis zones (TAZs), a gravitational logic estimation method for passenger flow volumes from and to bus stops was constructed with land use elements between bus stop TAZs and the upstream and downstream collections of bus stop TAZs. Building upon this, the passenger flow volumes from and to 38 bus stops in the Xueyuan Square area of Dalian during weekday morning peak hours were taken as the experimental objects. The basic estimation models of two gravity sets corresponding to passenger flow volumes from and to bus stops were constructed using the bus travel generation based on the aggregation of area-based origin unit method and the bus travel distance impedance based on the probability density method. Finally, the reliability of the estimation method of passenger flow volumes from and to bus stops was verified by regression fitting between the surveyed values of passenger flow volume and the estimated values of the basic models. The results indicate that the fuzzy estimation and transformation of bus travel based on land use elements, which serves as a crucial lever for facilitating strategic alignment in transit-oriented development (TOD), can be effectively achieved by using the area-based origin unit method to aggregate bus travel generation and the probability density method to evaluate the bus travel distance impedance.
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Misharin, A., D. Namiot, and O. Pokusaev. "On Passenger Flow Estimation for new Urban Railways." IOP Conference Series: Earth and Environmental Science 177 (August 10, 2018): 012012. http://dx.doi.org/10.1088/1755-1315/177/1/012012.
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Kagan, D. Z. "ESTIMATION OF DEPENDENCE OF PASSENGER TURNOVER OF TRANSPORT ON MACROECONOMIC FACTORS." World of Transport and Transportation 15, no. 1 (February 28, 2017): 140–49. http://dx.doi.org/10.30932/1992-3252-2017-15-1-12.
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[For the English abstract and full text of the article please see the attached PDF-File (English version follows Russian version)].ABSTRACT The instability of the domestic transport services market, significant fluctuations in demand on the part of the population, make it necessary to evaluate the range of problems under study with particular attention. The author analyzes the impact of macroeconomic factors on passenger traffic. The article reveals the high dependence of the total passenger flow on the economic condition of the country, the population’s solvency margin. The change in the strength of the connection between passenger turnover and GDP over the last 24 years is considered. It is suggested that there is some «inertia» of transportation indicators and, at the same time, the predominant coincidence of the dynamics of the gross domestic product and the total passenger flow is analytically proved. Keywords: transport, macroeconomic factors, passenger transportation, forecasting, GDP, passenger turnover, interdependence of indicators.
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Zhang, Na, Zijia Wang, Feng Chen, Jingni Song, Jianpo Wang, and Yu Li. "Low-Carbon Impact of Urban Rail Transit Based on Passenger Demand Forecast in Baoji." Energies 13, no. 4 (February 11, 2020): 782. http://dx.doi.org/10.3390/en13040782.
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There are increasing traffic pollution issues in the process of urbanization in many countries; urban rail transit is low-carbon and widely regarded as an effective way to solve such problems. The passenger flow proportion of different transportation types is changing along with the adjustment of the urban traffic structure and a growing demand from passengers. The reduction of carbon emissions brought about by rail transit lacks specific quantitative research. Based on a travel survey of urban residents, this paper constructed a method of estimating carbon emissions from two different scenarios where rail transit is and is not available. This study uses the traditional four-stage model to forecast passenger volume demand at the city level and then obtains the basic target parameters for constructing the carbon emission reduction model, including the trip origin-destination (OD), mode, and corresponding distance range of different modes on the urban road network. This model was applied to Baoji, China, where urban rail transit will be available from 2023. It calculates the changes in carbon emission that rail transit can bring about and its impact on carbon emission reductions in Baoji in 2023.
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Raj, Jithin. "Estimation of PCU Values for Urban Roads by Considering the Effect of Signalized Intersections under Mixed Traffic Conditions." European Transport/Trasporti Europei, no. 86 (March 2022): 1–17. http://dx.doi.org/10.48295/et.2022.86.6.
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Along with multiple classes of vehicles, frequent signalized intersections on the urban roads and the platoons thus created add complexity to the estimation of Passenger Car Units (PCU). Under such scenarios of interrupted traffic, a new approach based on platoon movement of vehicles is introduced for the realistic estimation of PCU values for urban roads by incorporating the appropriate vehicle behavior and interactions. The PCU values were derived by finding the trade-off between the speed reduction caused by the flow of passenger cars and other vehicle classes, as per the given definition by Transport Research Laboratory (TRL). However, the speed-flow, as well as the speed-density relations, were analyzed and found that later is preferred for the realistic estimation of PCUs for urban roads. The speed reduction is modeled using multiple linear regression with field data collected from typical urban roads characterized by platoon flow in Mumbai city (India).
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Alina Gennadievna, Loktionova, and Shevtsova Anastasia Gennadievna. "ESTIMATION OF TECHNICAL PARAMETERS OF CARS IN THE TRAFFIC FLOW." World of transport and technological machines 2(79), no. 4 (2022): 75–80. http://dx.doi.org/10.33979/2073-7432-2022-2(79)-4-75-80.
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SUGIYAMA, Yoichi, Hiroshi MATSUBARA, Shuichi MYOJO, Kazuki TAMURA, and Naoya OZAKI. "An Approach for Real-time Estimation of Railway Passenger Flow." Quarterly Report of RTRI 51, no. 2 (2010): 82–88. http://dx.doi.org/10.2219/rtriqr.51.82.
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Ji, Yuxiong, Jizhou Zhao, Zhiming Zhang, and Yuchuan Du. "Estimating Bus Loads and OD Flows Using Location-Stamped Farebox and Wi-Fi Signal Data." Journal of Advanced Transportation 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6374858.
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Electronic fareboxes integrated with Automatic Vehicle Location (AVL) systems can provide location-stamped records to infer passenger boarding at individual stops. However, bus loads and Origin-Destination (OD) flows, which are useful for route planning, design, and real-time controls, cannot be derived directly from farebox data. Recently, Wi-Fi sensors have been used to collect passenger OD flow information. But the data are insufficient to capture the variation of passenger demand across bus trips. In this study, we propose a hierarchical Bayesian model to estimate trip-level OD flow matrices and a period-level OD flow matrix using sampled OD flow data collected by Wi-Fi sensors and boarding data provided by fareboxes. Bus loads on each bus trip are derived directly from the estimated trip-level OD flow matrices. The proposed method is evaluated empirically on an operational bus route and the results demonstrate that it provides good and detailed transit route-level passenger demand information by combining farebox and Wi-Fi signal data.
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Feng, Xuesong, Hemeizi Zhang, Tiantian Gan, Qipeng Sun, Fei Ma, and Xun Sun. "RANDOM COEFFICIENT MODELING RESEARCH ON SHORT-TERM FORECAST OF PASSENGER FLOW INTO AN URBAN RAIL TRANSIT STATION." TRANSPORT 31, no. 1 (March 22, 2016): 94–99. http://dx.doi.org/10.3846/16484142.2016.1128484.
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Taking a representative metro station in Beijing as example, this research has newly developed a random coefficient model to predict the short-term passenger flows with sudden increases sometimes into an urban rail transit station. The hierarchical Bayesian approach is iteratively applied in this work to estimate the new model and the estimation outcomes in each of the iterative calibrations are improved by sequential Bayesian updating. It has been proved that the estimation procedure is able to effectively converge to rational results with satisfying accuracies. In addition, the model application study reveals that besides sufficient preparations in manpower, devices, etc.; the information of the factors affecting the passenger flows into an urban rail transit station should be timely transferred in advance from important buildings, road intersections, squares and so on in neighborhood to this station. In this way, this station is able to cope with the unexpectedly sharp increases of the passenger flows into the station to ensure its operation safety.
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Wang, Chao, Bao Ming Han, Fang Lu, and Hua Li. "Estimating of the Distribution Rate of Passenger Flow on Shenyang-Dalian Railway after Operation of Passenger Dedicated Line." Applied Mechanics and Materials 253-255 (December 2012): 1581–85. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1581.
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The methods in common use for estimating the distribution rate of passenger flow are distribution rate curve method and the function model method. Logit model method is adopted by comparison and selection. The solutions of eigenfunction in traditional model are improved. The utility function of transport product is applied instead of the traditionally used eigenfunction in the Logit model. The distribution rate of passenger transport demand for railway around operation of the passenger dedicated line in Shenyang-Dalian passenger transport market is 55.73% and 69.21% respectively, which is obtained by using improved Logit model. After the operation of the passenger dedicated line, the balance of supply and demand of railway transport product between Shenyang and Dalian can be solved combined with the investigation results of passenger flow.
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Wang, Zihan, and Yanguang Chen. "Exploring Spatial Patterns of Interurban Passenger Flows Using Dual Gravity Models." Entropy 24, no. 12 (December 8, 2022): 1792. http://dx.doi.org/10.3390/e24121792.
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Geographical gravity models can be employed to quantitatively describe and predict spatial flows, including migration flows, passenger flows, daily commuting flows, etc. However, how to model spatial flows and reveal the structure of urban traffic networks in the case of missing partial data is still a problem to be solved. This paper is devoted to characterizing the interurban passenger flows in the Beijing–Tianjin–Hebei region of China using dual gravity models and Tencent location big data. The method of parameter estimation is the least squares regression. The main results are as follows. First, both the railway and highway passenger flows can be effectively described by dual gravity models. A small part of missing spatial data can be compensated for by predicted values. Second, the fractal properties of traffic flows can be revealed. The railway passenger flows follow the gravity scaling law better than the highway passenger flows. Third, the prediction residuals indicate the changing trend of interurban connections in the study area in recent years. The center of gravity of the spatial dynamics has shifted from the Beijing–Tianjin–Tangshan triangle to the Beijing–Baoding–Shijiazhuang axis. A conclusion can be reached that the dual gravity model is an effective tool for analyzing spatial structures and dynamics of traffic networks and flows. Moreover, the model provides a new approach to estimating the fractal dimensions of traffic networks and spatial flow patterns.
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Gong, Wenan, Ting Zeng, Haina Song, Jiayi Su, Honggang Wang, Lin Hu, Jinchao Xiao, Xiaosong Liu, Ming Li, and Jingfeng Yang. "A Bus-Scheduling Method Based on Multi-Sensor Data Fusion and Payment Authenticity Verification." Electronics 11, no. 10 (May 10, 2022): 1522. http://dx.doi.org/10.3390/electronics11101522.
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It is of great significance to ensure public transportation management capabilities by improving urban public transport services. One method is to solve the problems related to the quality of data submitted for public funding as well as the accuracy and transparency of the supervision and review processes; moreover, improving public-transportation-service systems is a viable method to solve such problems. With technological advancements and the application of new technologies such as automatic driving and multiple payment, it has gradually become difficult for user-data verification systems, based on the original single bus payment method, to cater to these new technologies. Diversified payment and complex management methods have highlighted the need for new verification methods. Firstly, in this paper, we constructed the Origin–Destination (OD) model of bus-passenger flows by using real-time transmission of passenger-multiple-payment data, on-board-video passenger flow detection data and vehicle real-time positioning data. On this basis, the bus waybill data of other intelligent bus systems and the wait data of bus stations were integrated, so as to establish the travel chain theory by matching passenger flow and the temporal and spatial distribution model. Then, an OD analysis of public-transport passenger flows could be carried out, with a detailed analysis of vehicle, station and line-passenger flow, and the travel characteristics of public transport passenger flow could be excavated. Then, according to the means-end chain theory, the spatiotemporal distribution of the passenger flow data was obtained to carry out an OD analysis of the passenger flow, so as to perform a refinement analysis of the vehicle, station, and passenger flow. Thereby, the characteristics of the passenger flow were explored. Subsequently, payment-authenticity-verification models were established for the data-validity assessment, video-data analysis, passenger-flow estimation, and early warnings in order to determine the authenticity of the payment data. Lastly, based on the multi-sensor passenger flow data fusion and the data authenticity verification models, combined with the application of new technologies such as the use of autonomous buses, the test was promoted. That is, by taking intelligent bus scheduling as the scenario, the research method was tested and verified with real-time passenger flow data according to historical data. The results showed that the method accurately predicted the passenger flow, and had a positive role in improving the efficiency of payment-data-authenticity verification. The application of the method can enhance the management and service quality of public transportation.
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Kahali, Dharitri, and Rajat Rastogi. "Passenger Flows at Escalators – Arriving at Count Interval for Design Flow Estimation." European Journal of Transport and Infrastructure Research 21, no. 4 (November 10, 2021): 62–80. http://dx.doi.org/10.18757/ejtir.2021.21.4.5703.
Full textAbstract:
Selection of a count interval to extract flow data on pedestrian facilities seems to be governed by the operational environment of the facility and context of the study. Based on the flow condition, which may be intermittent, uniform, or periodic, the count interval has been found varying between 1 minute and 5 minutes. For instantaneous peaks, it is reduced to even 10s. The selection of a count interval will impact the flow values and subsequently the design requirements and operational efficiency. Present study, in this light, focusses on escalators located at metro rail stations. The study region is Delhi, India. Based on the analysis, the count interval for data extraction is recommended as 24 seconds, which is expected to result in a flow that does not cause unnecessary increase in facility size and keep it usable for most of the time. The absolute design flow value may be considered between 140–148 ped/m/min, which is the 5th highest rank order peak flow. The results are expected to optimize the resources, both for data collection and size of a facility.
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Fu, Xianlei, Maozhi Wu, Sasthikapreeya Ponnarasu, and Limao Zhang. "A Hybrid Deep Learning Approach for Real-Time Estimation of Passenger Traffic Flow in Urban Railway Systems." Buildings 13, no. 6 (June 12, 2023): 1514. http://dx.doi.org/10.3390/buildings13061514.
Full textAbstract:
This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning neural network named the graph convolutional memory network (GCMN) was constructed and trained for accurate real-time prediction of passenger traffic flow for the MRS. Data collected of the traffic flow in Delhi’s metro rail network system in the period from October 2012 to May 2017 were utilized to demonstrate the effectiveness of the developed model. The results indicate that (1) the developed method provides accurate predictions of the traffic flow with an average coefficient of determination (R2) of 0.920, RMSE of 368.364, and MAE of 549.527, and (2) the GCMN model outperforms state-of-the-art methods, including LSTM and the light gradient boosting machine (LightGBM). This study contributes to the state of practice in proposing a novel framework that provides reliable estimations of passenger traffic flow. The developed model can also be used as a benchmark for planning and upgrading works of the MRS by metro owners and architects.