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Добірка наукової літератури з теми "Passengers Flow Estimation"

Автор: Grafiati

Опубліковано: 28 вересня 2024

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Статті в журналах з теми "Passengers Flow Estimation"

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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Анотація:

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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Анотація:

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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Анотація:

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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Більше джерел

Дисертації з теми "Passengers Flow Estimation"

Drosouli, Ifigeneia. "Multimodal machine learning methods for pattern analysis in smart cities and transportation." Electronic Thesis or Diss., Limoges, 2024. http://www.theses.fr/2024LIMO0028.

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Dans le contexte des environnements urbains modernes et densément peuplés, la gestion efficace des transports et la structure des Systèmes de Transport Intelligents (STI) sont primordiales. Le secteur des transports publics connaît actuellement une expansion et une transformation significatives dans le but d'améliorer l'accessibilité, d'accommoder des volumes de passagers plus importants sans compromettre la qualité des déplacements, et d'adopter des pratiques respectueuses de l'environnement et durables. Les avancées technologiques, notamment dans l'Intelligence Artificielle (IA), l'Analyse de Données Massives (BDA), et les Capteurs Avancés (CA), ont joué un rôle essentiel dans la réalisation de ces objectifs et ont contribué au développement, à l'amélioration et à l'expansion des Systèmes de Transport Intelligents. Cette thèse aborde deux défis critiques dans le domaine des villes intelligentes, se concentrant spécifiquement sur l'identification des modes de transport utilisés par les citoyens à un moment donné et sur l'estimation et la prédiction du flux de transport au sein de divers systèmes de transport. Dans le contexte du premier défi, deux approches distinctes ont été développées pour la Détection des Modes de Transport. Tout d'abord, une approche d'apprentissage approfondi pour l'identification de huit médias de transport est proposée, utilisant des données de capteurs multimodaux collectées à partir des smartphones des utilisateurs. Cette approche est basée sur un réseau Long Short-Term Memory (LSTM) et une optimisation bayésienne des paramètres du modèle. À travers une évaluation expérimentale approfondie, l'approche proposée démontre des taux de reconnaissance remarquablement élevés par rapport à diverses approches d'apprentissage automatique, y compris des méthodes de pointe. La thèse aborde également des problèmes liés à la corrélation des caractéristiques et à l'impact de la réduction de la dimensionnalité. La deuxième approche implique un modèle basé sur un transformateur pour la détection des modes de transport appelé TMD-BERT. Ce modèle traite l'ensemble de la séquence de données, comprend l'importance de chaque partie de la séquence d'entrée, et attribue des poids en conséquence en utilisant des mécanismes d'attention pour saisir les dépendances globales dans la séquence. Les évaluations expérimentales mettent en évidence les performances exceptionnelles du modèle par rapport aux méthodes de pointe, soulignant sa haute précision de prédiction. Pour relever le défi de l'estimation du flux de transport, un Réseau Convolutif Temporel et Spatial (ST-GCN) est proposé. Ce réseau apprend à la fois des données spatiales du réseau de stations et des séries temporelles des changements de mobilité historiques pour prédire le flux de métro urbain et le partage de vélos à un moment futur. Le modèle combine des Réseaux Convolutifs Graphiques (GCN) et des Réseaux Long Short-Term Memory (LSTM) pour améliorer la précision de l'estimation. Des expériences approfondies menées sur des ensembles de données du monde réel du système de métro de Hangzhou et du système de partage de vélos de la ville de New York valident l'efficacité du modèle proposé, démontrant sa capacité à identifier des corrélations spatiales dynamiques entre les stations et à faire des prévisions précises à long terme
In the context of modern, densely populated urban environments, the effective management of transportation and the structure of Intelligent Transportation Systems (ITSs) are paramount. The public transportation sector is currently undergoing a significant expansion and transformation with the objective of enhancing accessibility, accommodating larger passenger volumes without compromising travel quality, and embracing environmentally conscious and sustainable practices. Technological advancements, particularly in Artificial Intelligence (AI), Big Data Analytics (BDA), and Advanced Sensors (AS), have played a pivotal role in achieving these goals and contributing to the development, enhancement, and expansion of Intelligent Transportation Systems. This thesis addresses two critical challenges within the realm of smart cities, specifically focusing on the identification of transportation modes utilized by citizens at any given moment and the estimation and prediction of transportation flow within diverse transportation systems. In the context of the first challenge, two distinct approaches have been developed for Transportation Mode Detection. Firstly, a deep learning approach for the identification of eight transportation media is proposed, utilizing multimodal sensor data collected from user smartphones. This approach is based on a Long Short-Term Memory (LSTM) network and Bayesian optimization of model’s parameters. Through extensive experimental evaluation, the proposed approach demonstrates remarkably high recognition rates compared to a variety of machine learning approaches, including state-of-the-art methods. The thesis also delves into issues related to feature correlation and the impact of dimensionality reduction. The second approach involves a transformer-based model for transportation mode detection named TMD-BERT. This model processes the entire sequence of data, comprehends the importance of each part of the input sequence, and assigns weights accordingly using attention mechanisms to grasp global dependencies in the sequence. Experimental evaluations showcase the model's exceptional performance compared to state-of-the-art methods, highlighting its high prediction accuracy. In addressing the challenge of transportation flow estimation, a Spatial-Temporal Graph Convolutional Recurrent Network is proposed. This network learns from both the spatial stations network data and time-series of historical mobility changes to predict urban metro and bike sharing flow at a future time. The model combines Graph Convolutional Networks (GCN) and Long Short-Term Memory (LSTM) Networks to enhance estimation accuracy. Extensive experiments conducted on real-world datasets from the Hangzhou metro system and the NY City bike sharing system validate the effectiveness of the proposed model, showcasing its ability to identify dynamic spatial correlations between stations and make accurate long-term forecasts

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Lu, Dawei. "Route Level Bus Transit Passenger Origin-Destination Flow Estimation Using Apc Data: Numerical And Empirical Investigations." Columbus, Ohio : Ohio State University, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228268640.

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Chen, Aijing. "Bus Transit Passenger Origin-Destination Flow Estimation: Capturing Terminal Carry-Over Movements Using the Iterative Proportional Fitting Method." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1593675738643412.

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Strohl, Brandon A. "Empirical Assessment of the Iterative Proportional Fitting Method for Estimating Bus Route Passenger Origin-Destination Flows." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1261583295.

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Ji, Yuxiong. "Distribution-based Approach to Take Advantage of Automatic Passenger Counter Data in Estimating Period Route-level Transit Passenger Origin-Destination Flows:Methodology Development, Numerical Analyses and Empirical Investigations." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1299688722.

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Lu, Shiu-chun, and 盧修群. "Passenger Car Equivalent Estimation of Motorcycle by Measuring the Queue Clearing Time - An Example for the Mixed Traffic Flow of Taiwan Boulevard in Taichung City." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/92069603979042120397.

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Анотація:

碩士
逢甲大學
運輸科技與管理學系
101
If the passenger car equivalent settings (large vehicle equals to 1.5 PCE; passenger car equals to 1 PCE; motorcycle equals to 0.3 PCE) of general practice is assumed for urban timing plan, there may be discrepancies between the simulated flow and real traffic flow. Especially the influence of the number of motorcycles may be biased. Due to high ownership of private vehicles, dense population, and limited GDP (Gross Domestic Product) in Taiwan, the number of motorcycles on the road are higher than that of other countries. In mixed traffic lanes, how motorcycles stop or wait for entering intersections in front of cars, and how motorcycles affect movement of traffic flow is an important issue. Through observation and investigation, this study obtained data within research scope, and made the assumptions of “no vehicle moves before red light turns green”, “the queue clearing time of fast lane and that of mixed traffic lane can be compared to estimate the PCE value of motorcycle in mixed traffic lanes”, “when the first car waiting in the mixed traffic lane arrives at the stop line, it means that the motorcycles in front of the car have been all cleared”, “the motorcycles behind or alongside the first car waiting in the mixed traffic lane have no impact on the queue clearing of the cars”, “vehicle queue clearing time has linear relation with PCE value”, and “The influence of vehicle lane changing behavior during the queue clearing process is not considered.”. This study then used most of samples in calibration process to explore the correlation between the number of motorcycles and queue clearing time, and calibrated the PCE value for motorcycle. Moreover, this study used remaining samples in validation process by using relative difference percent method. Finally, according to the research findings, the PCE value for the intersection of Taiwan Boulevard and Wenxin Road is in the range of 0.25~0.27, and the relative difference percent value is in the range of 14%~20%, which is a reasonable prediction. The PCE value for the intersection of Taiwan Boulevard and Dadung Road is in the range of 0.28~0.63, and the relative difference percent value is in the range of 8%~24%, which is also a reasonable prediction.

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Частини книг з теми "Passengers Flow Estimation"

Heyde, T., J. Behne, G. Dettweiler, and F. Neumann. "User Support for Estimating the Passenger Flow in Airport Terminals." In Human Comfort and Security of Information Systems, 174–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60665-6_18.

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Cai, Yuexiao, Yunlong Zhao, Jinqian Yang, and Changxin Wang. "A Bus Passenger Flow Estimation Method Based on POI Data and AFC Data Fusion." In Communications in Computer and Information Science, 352–67. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7530-3_27.

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Kong, Chaoqun, Tangyi Guo, and Liu He. "Research on OD Estimation of Public Transit Passenger Flow Based on Multi-source Data." In Lecture Notes in Electrical Engineering, 589–603. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5615-7_42.

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Ye, Jiexia, JuanJuan Zhao, Liutao Zhang, ChengZhong Xu, Jun Zhang, and Kejiang Ye. "A Data-Driven Method for Dynamic OD Passenger Flow Matrix Estimation in Urban Metro Systems." In Big Data – BigData 2020, 116–26. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59612-5_9.

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Peng, Yu Fei, and Xi Jiang. "Passenger Flow Estimation in Urban Rail Transit Transfer Station Based on Multi-Source Detection Data." In Lecture Notes in Electrical Engineering, 279–89. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-6431-4_24.

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Moridpour, Sara. "Analysing the Performance of a Fuzzy Lane Changing Model Using Data Mining." In Data Mining in Dynamic Social Networks and Fuzzy Systems, 289–315. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4213-3.ch013.

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Анотація:

Heavy vehicles have substantial impact on traffic flow particularly during heavy traffic conditions. Large amount of heavy vehicle lane changing manoeuvres may increase the number of traffic accidents and therefore reduce the freeway safety. Improving road capacity and enhancing traffic safety on freeways has been the motivation to establish heavy vehicle lane restriction strategies to reduce the interaction between heavy vehicles and passenger cars. In previous studies, different heavy vehicle lane restriction strategies have been evaluated using microscopic traffic simulation packages. Microscopic traffic simulation packages generally use a common model to estimate the lane changing of heavy vehicles and passenger cars. The common lane changing models ignore the differences exist in the lane changing behaviour of heavy vehicle and passenger car drivers. An exclusive fuzzy lane changing model for heavy vehicles is developed and presented in this chapter. This fuzzy model can increase the accuracy of simulation models in estimating the macroscopic and microscopic traffic characteristics. The results of this chapter shows that using an exclusive lane changing model for heavy vehicles, results in more reliable evaluation of lane restriction strategies.

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Hoang, Trung, Donato Di Paola, and Anthony Ohazulike. "Distributed Vision-Based Passenger Flow Monitoring System for Light Rail Networks." In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde230009.

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In this paper, we present an innovative approach to passenger flow monitoring for light rail transportation networks. We propose a distributed system based on two main concepts. On each vehicle in the transportation network, a set of sensors is used to count people at a given place. On a cloud-based server, a data synchronization and storage system aggregates the data sent from all vehicles and provides a global view of the transportation network. The contribution, with respect to the state of the art, of our approach is twofold. First, the proposed distributed architecture is able to reduce the system global cost via its flexibility and ease of deployment, since the main part of the system is onboard each vehicle and not fixed at stations or track sections. Second, the novel vision-based passenger counting approach guarantees high levels of reliability in the estimation of the number of people in a given area, and the ability to provide real-time data on the global transportation network. Experimental results demonstrate the validity and the advantages of the proposed approach, paving way to future uses of the system as the base of additional network optimization modules for the global light rail transportation.

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Mustaffa, Zahiraniza, Ebrahim Hamid Hussein Al-Qadami, Syed Muzzamil Hussain Shah, and Khamaruzaman Wan Yusof. "Impact and Mitigation Strategies for Flash Floods Occurrence towards Vehicle Instabilities." In Flood Impact Mitigation and Resilience Enhancement. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.92731.

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This chapter presents a flood risk management system for vehicles at roadways, developed from extensive experimental and numerical studies on the impact of flash floods towards vehicle instabilities. The system, easily addressed as FLO-LOW, developed to contradict the assumptions that a vehicle would be able to protect the passengers from the flood impact. Herein the hydrodynamics of flows moving across these roads coupled with the conditions of a static car that would result in vehicle instabilities has been studied. In an attempt to prevent fatalities in commonly flooded areas, permanent structures are installed to warn users regarding water depth at the flooded areas. The existing flood monitoring system only focuses on water conditions in rivers or lake in order to determine risks associated with floods. Thus, there is a need for a better system to understand and quantify a mechanism to determine hydrodynamics instability of a vehicle in floodwaters. FLO-LOW enables the road users to input their vehicle information for a proper estimation of safety limits upon crossing the flood prone area. Preferably, the system enables road users to describe and quantify parameters that might cause their vehicles to become vulnerable to being washed away as they enter the flooded area.

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Akin, Darcin, and Serdar Alasalvar. "Estimate Urban Growth and Expansion by Modeling Urban Spatial Structure Using Hierarchical Cluster Analyses of Interzonal Travel Data." In Megacities and Rapid Urbanization, 518–48. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-5225-9276-1.ch026.

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Estimating the spatial organization of cities yields insights into interactions over a spatial structure, and thus creating efficient subcenters with more balanced distribution of travel patterns over urban agglomerations can be exercised via models which support an evidence-based spatial planning. As cities evolve and self-organize as complex spatial structures, problems such as accessibility, environmental sustainability, and social equity or weak economy can be incurred by unrealistic development scenarios. In this regard, it is claimed that the dynamic nature of the urban spatial structure can to be modeled to estimate growth and expansion of it using the patterns of freight and passenger movements throughout metropolitan areas under the assumption that there is a simple and straightforward link between travel flows and urban spatial structure. The main effort of this study is to describe and model urban spatial structure and its evolution due to the spatial distribution of population, and employment centers.

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Тези доповідей конференцій з теми "Passengers Flow Estimation"

"A FUZZY LOGIC INFERENCE APPROACH FOR THE ESTIMATION OF THE PASSENGERS FLOW DEMAND." In International Conference on Fuzzy Computation. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003057701250129.

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Hoffer, P. A., T. Deconinck, Ch Hirsch, B. Ortun, S. Canard-Caruana, G. Rahier, S. Pascal, and B. Caruelle. "Aeroacoustic Computations of Contra-Rotating Open Rotors Using the Nonlinear Harmonic Method and a Chorochronic Approach." In ASME Turbo Expo 2012: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/gt2012-68982.

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Due to their great potential for fuel saving, Contra-Rotating Open Rotors (CRORs) receive renewed interest by the airframers and the engine manufacturers. The inherent high efficiency of this propulsion system, however, is potentially offset by the level of noise emitted by the open blades. The acoustic impact on passengers and community may represent a major issue to their environmental acceptance. Fast and robust noise prediction tools are clearly required to support the development of quieter propellers and their integration in future civil aeronautical transport. The most common strategy for noise estimation consists in a two-step approach, based on the Lighthill analogy: unsteady near-field aerodynamic flow simulation to evaluate the noise sources, coupled to a far-field acoustic propagation code. Focus is given here on two structured grid flow solvers employed to investigate a scale-model of a 12×10 pusher CROR. The unsteady aerodynamic three-dimensional flow is indeed computed for typical cruise conditions using both the nonlinear harmonic method (NLH) of FINE™/Turbo software and elsA’s chorochronic technique. The evaluation of the far-field noise based on the aerodynamic fields is then carried out with the KIM code, Onera’s acoustic propagation code based on the Ffowcs-Williams and Hawkings (FW-H) formulation. The obtained results enable an analysis of the complex aerodynamic interactions between the two propellers that generate interaction tones in the acoustic signature of the propulsion system. A comparison in terms of numerical settings, computational costs and flow fields is performed between the two CFD methods, which show an excellent match of the predicted global performance of the propulsion system. Some differences in the predicted acoustic signatures are discussed in the paper.

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Chung, Wei-Yi, Yen-Nan Ho, Yu-Hsuan Wu, and Jheng-Long Wu. "A Dynamic Embedding Method for Passenger Flow Estimation." In 2021 10th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2021. http://dx.doi.org/10.1109/iiai-aai53430.2021.00070.

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Shimada, Yutaka, Motoki Takagi, and Yukinobu Taniguchi. "Person Re-identification for Estimating Bus Passenger Flow." In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2019. http://dx.doi.org/10.1109/mipr.2019.00037.

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Nagasaki, Y., M. Asuka, and K. Komaya. "A fast method for estimating railway passenger flow." In COMPRAIL 2006. Southampton, UK: WIT Press, 2006. http://dx.doi.org/10.2495/cr060181.

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Zhang, Jian, Wenquan Li, and Jinhuan Zhao. "Estimation of Original-Destination Matrices for Public Traffic Passenger Flow." In Ninth International Conference of Chinese Transportation Professionals (ICCTP). Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41064(358)248.

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Komatsu, Shunta, Ryosuke Furuta, and Yukinobu Taniguchi. "Passenger Flow Estimation with Bipartite Matching on Bus Surveillance Cameras." In 2021 IEEE 4th International Conference on Multimedia Information Processing and Retrieval (MIPR). IEEE, 2021. http://dx.doi.org/10.1109/mipr51284.2021.00038.

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Xie, Hui, and Yuan Gao. "Simulation Based Estimation Approach for Departure Passenger Flow at Airport Terminal." In 2015 8th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2015. http://dx.doi.org/10.1109/iscid.2015.256.

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Yu, Chang, and Zhao-cheng He. "Passenger Flow Estimation Based on Smart Card Data in Public Transit." In 14th COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413623.064.

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Vidya, G. S., V. S. Hari, and Suryakumar Shivasagaran. "Estimation of Passenger Flow in a Bus Route using Kalman Filter." In 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2020. http://dx.doi.org/10.1109/icaccs48705.2020.9074363.

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Звіти організацій з теми "Passengers Flow Estimation"

Videa, Aldo, and Yiyi Wang. Inference of Transit Passenger Counts and Waiting Time Using Wi-Fi Signals. Western Transportation Institute, August 2021. http://dx.doi.org/10.15788/1715288737.

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Passenger data such as real-time origin-destination (OD) flows and waiting times are central to planning public transportation services and improving visitor experience. This project explored the use of Internet of Things (IoT) Technology to infer transit ridership and waiting time at bus stops. Specifically, this study explored the use of Raspberry Pi computers, which are small and inexpensive sets of hardware, to scan the Wi-Fi networks of passengers’ smartphones. The process was used to infer passenger counts and obtain information on passenger trajectories based on Global Positioning System (GPS) data. The research was conducted as a case study of the Streamline Bus System in Bozeman, Montana. To evaluate the reliability of the data collected with the Raspberry Pi computers, the study conducted technology-based estimation of ridership, OD flows, wait time, and travel time for a comparison with ground truth data (passenger surveys, manual data counts, and bus travel times). This study introduced the use of a wireless Wi-Fi scanning device for transit data collection, called a Smart Station. It combines an innovative set of hardware and software to create a non-intrusive and passive data collection mechanism. Through the field testing and comparison evaluation with ground truth data, the Smart Station produced accurate estimates of ridership, origin-destination characteristics, wait times, and travel times. Ridership data has traditionally been collected through a combination of manual surveys and Automatic Passenger Counter (APC) systems, which can be time-consuming and expensive, with limited capabilities to produce real-time data. The Smart Station shows promise as an accurate and cost-effective alternative. The advantages of using Smart Station over traditional data collection methods include the following: (1) Wireless, automated data collection and retrieval, (2) Real-time observation of passenger behavior, (3) Negligible maintenance after programming and installing the hardware, (4) Low costs of hardware, software, and installation, and (5) Simple and short programming and installation time. If further validated through additional research and development, the device could help transit systems facilitate data collection for route optimization, trip planning tools, and traveler information systems.

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Uechi, Luis, and José A. Barbero. Assessment of Transport Data Availability and Quality in Latin America. Inter-American Development Bank, January 2012. http://dx.doi.org/10.18235/0010453.

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The report is part of an initiative to map the transport data currently available within selected developing countries, looking to evaluate availability and quality of transport-related data in general, and particularly for information gaps affecting estimation of Greenhouse Gas emissions. The assessment of transport data availability and quality is performed by comparing the input information needed to run a transport model that identifies the major drivers for transport activity and emissions. The database requested includes typical transport information like fleet size and composition, passenger and freight activity, and fuel consumption, as well as other types of data related to demographic, macroeconomic, trade or other variables specified by the model. The assessment was carried out in eight Latin American countries, checking the data that has been collected and reviewing gathering procedures. The results show the major data availability and quality gaps, and expose implications beyond the modeling of transport emissions: such data will provide a basis for the implementation of important transport sector policies and planning processes, affecting both the public and private sectors. The analysis has demonstrated that transportation systems in the region are relatively sophisticated, including a great diversity of modes, flows, vehicle types, fuel types, and so forth. Therefore, estimation of sector emissions is expected to be considerably data-intensive.

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