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1
Xu, Jiajie, Saijun Xu, Rui Zhou, Chengfei Liu, An Liu, and Lei Zhao. "TAML: A Traffic-aware Multi-task Learning Model for Estimating Travel Time." ACM Transactions on Intelligent Systems and Technology 12, no. 6 (December 31, 2021): 1–14. http://dx.doi.org/10.1145/3466686.
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Travel time estimation has been recognized as an important research topic that can find broad applications. Existing approaches aim to explore mobility patterns via trajectory embedding for travel time estimation. Though state-of-the-art methods utilize estimated traffic condition (by explicit features such as average traffic speed) for auxiliary supervision of travel time estimation, they fail to model their mutual influence and result in inaccuracy accordingly. To this end, in this article, we propose an improved traffic-aware model, called TAML, which adopts a multi-task learning network to integrate a travel time estimator and a traffic estimator in a shared space and improves the accuracy of estimation by enhanced representation of traffic condition, such that more meaningful implicit features are fully captured. In TAML, multi-task learning is further applied for travel time estimation in multi-granularities (including road segment, sub-path, and entire path). The multiple loss functions are combined by considering the homoscedastic uncertainty of each task. Extensive experiments on two real trajectory datasets demonstrate the effectiveness of our proposed methods.
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Yi, Ting, and Billy M. Williams. "Dynamic Traffic Flow Model for Travel Time Estimation." Transportation Research Record: Journal of the Transportation Research Board 2526, no. 1 (January 2015): 70–78. http://dx.doi.org/10.3141/2526-08.
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Travel time, as a fundamental measurement for intelligent transportation systems, is becoming increasingly important. Because of the wide deployment of fixed-point detectors on freeways, if travel time can be accurately estimated from point detector data, the indirect estimation method is cost-effective and widely applicable. This paper presents a modified dynamic traffic flow model for accurately estimating the travel time of freeway links under transition and congestion conditions with fixed-point detector data. The modified estimation model is based on a thorough analysis of the dynamic traffic flow model. The applications and the limitations of the model are analyzed for theory, equation derivation, and modifications. Through a simulation study and real traffic data, the (modified) dynamic models are compared according to performance measurements. A comparison of the estimated results and measurement errors shows the accuracy of the modified dynamic model for estimating the travel times of freeway links under transition and congestion traffic conditions.
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Ji, Yuxiong, Shengchuan Jiang, Yuchuan Du, and H. Michael Zhang. "Estimation of Bimodal Urban Link Travel Time Distribution and Its Applications in Traffic Analysis." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/615468.
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Vehicles travelling on urban streets are heavily influenced by traffic signal controls, pedestrian crossings, and conflicting traffic from cross streets, which would result in bimodal travel time distributions, with one mode corresponding to travels without delays and the other travels with delays. A hierarchical Bayesian bimodal travel time model is proposed to capture the interrupted nature of urban traffic flows. The travel time distributions obtained from the proposed model are then considered to analyze traffic operations and estimate travel time distribution in real time. The advantage of the proposed bimodal model is demonstrated using empirical data, and the results are encouraging.
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Xu, Tian-dong, Yuan Hao, Zhong-ren Peng, and Li-jun Sun. "Real-time travel time predictor for route guidance consistent with driver behavior." Canadian Journal of Civil Engineering 39, no. 10 (October 2012): 1113–24. http://dx.doi.org/10.1139/l2012-092.
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Providing reliable real-time travel time information is a critical challenge to all existing traffic routing systems. This study develops a new model for estimating and predicting real-time traffic conditions and travel times for variable message signs-based route guidance system. The proposed model is based on real-time limited detected traffic data, stochastic nonlinear macroscopic traffic flow model, and adaptive Kalman filtering theory. The method has the following main features: (1) real-time estimation and prediction of traffic conditions on a network level using limited traffic detectors, (2) travel time prediction in free flow and congested flow, and (3) prediction of drivers’ en-route diversion behavior. Field testing is conducted based on the Route Guidance Pilot Project sponsored by the National Science and Technology Ministry of China. The achieved testing results are satisfactory and have potential use for future works and field applications.
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Park, Dongjoo, Soyoung You, Jeonghyun Rho, Hanseon Cho, and Kangdae Lee. "Investigating optimal aggregation interval sizes of loop detector data for freeway travel-time estimation and prediction." Canadian Journal of Civil Engineering 36, no. 4 (April 2009): 580–91. http://dx.doi.org/10.1139/l08-129.
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With recent increases in the deployment of intelligent transportation system (ITS) technologies, traffic management centers have the ability to obtain and archive large amounts of data regarding the traffic system. These data can then be employed in estimations of current conditions and the prediction of future conditions on the roadway network. In this paper, we propose a general solution methodology for the identification of the optimal aggregation interval sizes of loop detector data for four scenarios (i) link travel-time estimation, (ii) corridor / route travel-time estimation, (iii) link travel-time forecasting, and (iv) corridor / route travel-time forecasting. This study applied cross validated mean square error (CVMSE) model for the link and route travel-time estimations, and a forecasting mean square error (FMSE) model for the link and corridor / route travel-time forecasting. These models were applied to loop detector data obtained from the Kyeongbu expressway in Korea. It was found that the optimal aggregation sizes for the travel-time estimation and forecasting were 3 to 5 min and 10 to 20 min, respectively.
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Gu, Jian, Miaohua Li, Linghua Yu, Shun Li, and Kejun Long. "Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data." Journal of Advanced Transportation 2021 (April 13, 2021): 1–19. http://dx.doi.org/10.1155/2021/8876626.
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In this paper, the calculation method of the link travel time is firstly analysed in the continuous traffic flow by using the detection data collected when vehicles pass through urban links, and a theoretical derivation formula for estimating link travel time is proposed by considering the typical vehicle travel time and the time headway deviation upstream and downstream of the links as the main parameters. A typical vehicle analysis method based on link travel time similarity is proposed, and the theoretical formula is optimized, respectively. Then, an estimation formula based on maximum travel time similarity and an estimation formula based on maximum travel time confidence interval similarity are proposed, respectively. Finally, when analysing the fitting conditions, the collected data from urban roads in Nanjing are used to verify the proposed travel time estimation method based on the radio frequency identification devices. The results show that time headway deviation converges to zero when the hourly vehicle volume is more than 20 veh/h in the certain flow direction, and there are more positive and negative fluctuations when the hourly vehicle volume is less than 10 veh/h in the certain flow direction. The accuracy of the proposed improved method based on typical vehicle travel time estimation is significantly improved by considering the typical vehicle travel time, and typical vehicles on the road segment mainly exist at the tail of the traffic platoon in the corresponding period.
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Nanthawichit, Chumchoke, Takashi Nakatsuji, and Hironori Suzuki. "Application of Probe-Vehicle Data for Real-Time Traffic-State Estimation and Short-Term Travel-Time Prediction on a Freeway." Transportation Research Record: Journal of the Transportation Research Board 1855, no. 1 (January 2003): 49–59. http://dx.doi.org/10.3141/1855-06.
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Traffic information from probe vehicles has great potential for improving the estimation accuracy of traffic situations, especially where no traffic detector is installed. A method for dealing with probe data along with conventional detector data to estimate traffic states is proposed. The probe data were integrated into the observation equation of the Kalman filter, in which state equations are represented by a macroscopic traffic-flow model. Estimated states were updated with information from both stationary detectors and probe vehicles. The method was tested under several traffic conditions by using hypothetical data, giving considerably improved estimation results compared to those estimated without probe data. Finally, the application of the proposed method was extended to the estimation and short-term prediction of travel time. Travel times were obtained indirectly through the conversion of speeds estimated or predicted by the proposed method. Experimental results show that the performance of travel-time estimation or prediction is comparable to that of some existing methods.
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Huang, Xiaohui, Pan He, Anand Rangarajan, and Sanjay Ranka. "Machine-Learning-Based Real-Time Multi-Camera Vehicle Tracking and Travel-Time Estimation." Journal of Imaging 8, no. 4 (April 6, 2022): 101. http://dx.doi.org/10.3390/jimaging8040101.
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Travel-time estimation of traffic flow is an important problem with critical implications for traffic congestion analysis. We developed techniques for using intersection videos to identify vehicle trajectories across multiple cameras and analyze corridor travel time. Our approach consists of (1) multi-object single-camera tracking, (2) vehicle re-identification among different cameras, (3) multi-object multi-camera tracking, and (4) travel-time estimation. We evaluated the proposed framework on real intersections in Florida with pan and fisheye cameras. The experimental results demonstrate the viability and effectiveness of our method.
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M. Ahmed, Rania, Zainab A. Alkaissi, and Ruba Y. Hussain. "TRAVEL TIME ANALYSIS OF SELECTED URBAN STREETS IN BAGHDAD CITY." Journal of Engineering and Sustainable Development 25, Special (September 20, 2021): 3–157. http://dx.doi.org/10.31272/jeasd.conf.2.3.15.
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Estimating travel time and measuring speed are critical for increasing the efficiency and safety of traffic road networks. This study presents an investigation of arterial travel time estimation for vital routes in Baghdad city. These estimations including speeds, stops, and delays were computed via GPS device and compared to those currently used to quantify congestion and travel time reliability. The study involved a 45-day survey of private vehicles in Baghdad utilizing a Global Positioning System (GPS) probe to collect data on traffic performance metrics for analysis in a GIS context. It was found that the proposed travel time performance measures show definite differences in estimates of peak-hour travel time as compared with weekend travel time. Route (1) from Bayaa intersection - Bab Al-Mutham intersection (through highway) produced a travel time of 165 minutes and 136 minutes for Bayaa intersection - Bab Al-Mutham intersection (through downtown). The travel speed of routes 1 and 2 are observed near 25 kmph which is below the local speed limit of 70 kmph. The maximum travel time of routes 1 and 2 are 71 minutes and 37 minutes, respectively. While delay time was observed 45 and 20 minutes due to traffic congestion on route 1 and 2, respectively. The majority of vehicles are capable of traveling at normal speeds, with relatively few exceeding them.
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Guo, Yajuan, and Licai Yang. "Reliable Estimation of Urban Link Travel Time Using Multi-Sensor Data Fusion." Information 11, no. 5 (May 16, 2020): 267. http://dx.doi.org/10.3390/info11050267.
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Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems.
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Li, Ruimin, Huajun Chai, and Jin Tang. "Empirical Study of Travel Time Estimation and Reliability." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/504579.
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This paper explores the travel time distribution of different types of urban roads, the link and path average travel time, and variance estimation methods by analyzing the large-scale travel time dataset detected from automatic number plate readers installed throughout Beijing. The results show that the best-fitting travel time distribution for different road links in 15 min time intervals differs for different traffic congestion levels. The average travel time for all links on all days can be estimated with acceptable precision by using normal distribution. However, this distribution is not suitable to estimate travel time variance under some types of traffic conditions. Path travel time can be estimated with high precision by summing the travel time of the links that constitute the path. In addition, the path travel time variance can be estimated by the travel time variance of the links, provided that the travel times on all the links along a given path are generated by statistically independent distributions. These findings can be used to develop and validate microscopic simulations or online travel time estimation and prediction systems.
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Suzuki, Hironori, Takashi Nakatsuji, Yordphol Tanaboriboon, and Kiyoshi Takahashi. "Dynamic Estimation of Origin-Destination Travel Time and Flow on a Long Freeway Corridor: Neural Kalman Filter." Transportation Research Record: Journal of the Transportation Research Board 1739, no. 1 (January 2000): 67–75. http://dx.doi.org/10.3141/1739-09.
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A model was formulated for estimating dynamic origin-destination (O-D) travel time and flow on a long freeway with a neural Kalman filter originally developed by the authors. The model predicts O-D travel times and flows simultaneously by using traffic detector data such as link traffic volumes, spot speeds, and off-ramp volumes. The model is based on a Kalman filter that consists of two equations: state and measurement. First, the state and measurement equations of the Kalman filter were modified to consider the influence of traffic states for some previous time steps. Then artificial neural network models were integrated with the Kalman filter to enable nonlinear formulations of the state and measurement equations. Finally, a macroscopic traffic flow simulation model was introduced to simulate traffic states on a freeway in advance and predict traffic variables such as O-D travel times, link traffic volumes, spot speeds, and off-ramp volumes. The new model was compared with a regression Kalman filter in which the state and measurement equations are defined by regression models. The numerical analysis indicated that the new model was capable of estimating nonlinearity of dynamic O-D travel time and flow and helped to improve their estimation precision under free-flow traffic states as well as congested flow states.
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Ludwig, C., J. Psotta, A. Buch, N. Kolaxidis, S. Fendrich, M. Zia, J. Fürle, A. Rousell, and A. Zipf. "TRAFFIC SPEED MODELLING TO IMPROVE TRAVEL TIME ESTIMATION IN OPENROUTESERVICE." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W7-2023 (June 22, 2023): 109–16. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w7-2023-109-2023.
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Abstract. Time-dependent traffic speed information at a street level is important for routing services to estimate accurate travel times and to recommend routes which avoid traffic congestion. Still, most open-source routing machines that use OpenStreetMap (OSM) as the primary data source rely on static driving speeds derived from OSM tags, since comprehensive traffic speed data is not openly available. In this study, a method was developed to model traffic speed by hour of day at a street level using open data from OpenStreetMap, Twitter and population data. The modelled traffic speed data was subsequently integrated into the open-source routing engine openrouteservice to improve travel time estimation in route planning. Machine learning models were trained for ten cities worldwide using traffic speed data from Uber Movement as reference data. Different indicators based on geolocation and timestamp of Twitter data as well as a geographically adapted betweeness centrality indicator were evaluated for their potential to improve prediction accuracy. In all cities, the Twitter indicators improved the model, although this effect was only visible for certain road types. The centrality indicator improved the model as well but to a lesser extent. The Google Routing API was used as reference to evaluate the accuracy in travel time estimation. Deviations in travel times were regionally different and were partly alleviated by including the raw traffic data by Uber or the modelled traffic speed data in openrouteservice.
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Luo, Sida. "Departure and travel time model for the temporal distribution of morning rush-hour traffic congestion." International Journal of Modern Physics C 31, no. 02 (December 30, 2019): 2050023. http://dx.doi.org/10.1142/s0129183120500230.
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The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers’ departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers’ unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules.
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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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Xia, Jing Xin, Wei Hua Zhang, and Dang Sheng Ma. "An Method to Urban Road Travel Time Estimation through ITS Data Fusion Based on D-S Evidential Theory." Applied Mechanics and Materials 488-489 (January 2014): 1419–25. http://dx.doi.org/10.4028/www.scientific.net/amm.488-489.1419.
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Focused on the current situations of the multiple traffic data collection efforts for urban roads, the link travel time estimation methods are respectively proposed based on two traffic data resources as station traffic data collected by microwave detectors and the vehicle plate data collected by the video vehicle plate identification system. Based on this, the link travel time estimation approach by fusing two data resources is presented using the Dempster-Shafer evidence reasoning theory, in which the probability distribution function is firstly used to construct the evidence function for each data resource, and then the weights for the two different data resources are estimated for link travel time fusion estimation through the combination rule of Dempster-Shafer evidence reasoning theory. Using the true link travel time collected by the test vehicles, the performance of the proposed method for link travel time estimation is evaluated. Evaluation results show that the proposed method can significantly improve the link travel time estimation accuracy when compared to the methods that merely uses single data resource.
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Fu, Fengjie, Dongfang Ma, Dianhai Wang, and Wei Qian. "An Optimization Method of Time Window Based on Travel Time and Reliability." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/921480.
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The dynamic change of urban road travel time was analyzed using video image detector data, and it showed cyclic variation, so the signal cycle length at the upstream intersection was conducted as the basic unit of time window; there was some evidence of bimodality in the actual travel time distributions; therefore, the fitting parameters of the travel time bimodal distribution were estimated using the EM algorithm. Then the weighted average value of the two means was indicated as the travel time estimation value, and the Modified Buffer Time Index (MBIT) was expressed as travel time variability; based on the characteristics of travel time change and MBIT along with different time windows, the time window was optimized dynamically for minimum MBIT, requiring that the travel time change be lower than the threshold value and traffic incidents can be detected real time; finally, travel times on Shandong Road in Qingdao were estimated every 10 s, 120 s, optimal time windows, and 480 s and the comparisons demonstrated that travel time estimation in optimal time windows can exactly and steadily reflect the real-time traffic. It verifies the effectiveness of the optimization method.
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Kim, Sunghoon, Hwapyeong Yu, and Hwasoo Yeo. "A Study on Travel Time Estimation of Diverging Traffic Stream on Highways Based on Timestamp Data." Journal of Advanced Transportation 2021 (January 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/8846634.
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Travel time is valuable information for both drivers and traffic managers. While properly estimating the travel time of a single road section, an issue arises when multiple traffic streams exist. In highways, this usually occurs at the upstream of diverge bottleneck. The aim of this paper is to provide a new framework for travel time estimation of a diverging traffic stream using timestamp data only. While providing the framework, the main focus of this paper is on performing a few analyses on the stage of travel time data classification in the proposed framework. Three sequential steps with a few statistical approaches are provided in this stage: detection of data divergence, classification of divergent data, and outlier filtering. First, a divergence detection index (DDI) of data has been developed, and the analysis results show that this new index is useful in finding the threshold of determining data divergence. Second, three different methods are tested in terms of properly classifying the divergent data. It is found that our modified method based on the approach used by Korea Expressway Corporation shows superior performance. Third, a polynomial regression-based method is used for outlier filtering, and this shows reasonable performance even at a relatively low market penetration rate (MPR) of probe vehicles. Then, the overall performance of the travel time estimation framework is tested, and this test demonstrates that the proposed framework can show improved performance in distinctively estimating the travel times of two different traffic streams in the same road section.
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Nnamani, Onyemaechi John, Victor Ayodele Ijaware, Joseph Olalekan Olusina, and Timothy Oluwadare Idowu. "Model for Estimating Travel Time on Dynamic Highway Networks in Akure, Ondo State Nigeria." European Journal of Engineering Research and Science 5, no. 3 (March 11, 2020): 275–81. http://dx.doi.org/10.24018/ejers.2020.5.3.1671.
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Travel time variability or distribution is very important to travel time reliability studies in transportation systems. This study aimed at developing a multivariate regression model for estimating travel times for dynamic highway networks in Akure Metropolis. The independent variables for the model are Traffic volume, density, speed of vehicles, and traffic flow while the dependent response variable is the Travel time. The estimated travel time was compared with the observed travel time from the real field data and the estimation using the regression model reveals a significant level of accuracy. Also, it was discovered that traffic volume, speed, density, and flow were highly correlated with travel time. The result analyzed using descriptive statistics in the SPSS software environment reveals an R2 value of 0.998, thereby indicating that the independent variables accounted for 99% of travel time in the study area. The Hypothesis tested at 95% confidence level using ANOVA unveils that there is no significant difference between the observed and estimated travel time model. The Mean Absolute Percentage Error (MAPE) of 0.049 shows that the model performed very well and was very efficient for analyzing the probabilistic relation between travel time and the independent variables. The study recommends the use of the developed travel time model for estimating travel time within the study area.
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Nnamani, Onyemaechi John, Victor Ayodele Ijaware, Joseph Olalekan Olusina, and Timothy Oluwadare Idowu. "Model for Estimating Travel Time on Dynamic Highway Networks in Akure, Ondo State Nigeria." European Journal of Engineering and Technology Research 5, no. 3 (March 11, 2020): 275–81. http://dx.doi.org/10.24018/ejeng.2020.5.3.1671.
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Travel time variability or distribution is very important to travel time reliability studies in transportation systems. This study aimed at developing a multivariate regression model for estimating travel times for dynamic highway networks in Akure Metropolis. The independent variables for the model are Traffic volume, density, speed of vehicles, and traffic flow while the dependent response variable is the Travel time. The estimated travel time was compared with the observed travel time from the real field data and the estimation using the regression model reveals a significant level of accuracy. Also, it was discovered that traffic volume, speed, density, and flow were highly correlated with travel time. The result analyzed using descriptive statistics in the SPSS software environment reveals an R2 value of 0.998, thereby indicating that the independent variables accounted for 99% of travel time in the study area. The Hypothesis tested at 95% confidence level using ANOVA unveils that there is no significant difference between the observed and estimated travel time model. The Mean Absolute Percentage Error (MAPE) of 0.049 shows that the model performed very well and was very efficient for analyzing the probabilistic relation between travel time and the independent variables. The study recommends the use of the developed travel time model for estimating travel time within the study area.
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Cheng, Juan, Gen Li, and Xianhua Chen. "Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests." Journal of Advanced Transportation 2019 (January 3, 2019): 1–13. http://dx.doi.org/10.1155/2019/8582761.
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Travel time of traffic flow is the basis of traffic guidance. To improve the estimation accuracy, a travel time estimation model based on Random Forests is proposed. 7 influence variables are viewed as candidates in this paper. Data obtained from VISSIM simulation are used to verify the model. Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. The result of variable importance shows that mean travel time of floating car t-f, traffic state parameter X, density of vehicle Kall, and median travel time of floating car tmenf are important variables affecting travel time of traffic flow; meanwhile other variables also have a certain influence on travel time. Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant.
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Yao, En Jian, Zhi Qiang Yang, Hong Na Dai, and Ting Zuo. "Estimation of Electric Vehicle's Crusing Range Based on Real-Time Links Average Speed." Applied Mechanics and Materials 361-363 (August 2013): 2100–2103. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.2100.
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For the reasons of relative short cruising range and insufficient charging facilities, the use and promotion of electric vehicles (EV) is restricted. The estimation of cruising range is important for the EV drivers when selecting the travel route. Energy consumption for different running status is the prerequisite for estimation of cruising range. In this study, an energy consumption factor model is established, which is characterized with reflecting the impact of frequent acceleration and deceleration of urban road, and the input parameter is easily obtained from usual road traffic information system. The results show that the proposed model can predict energy consumption with high accuracy. Then based on real-time links average travel speed, this paper proposes a method of estimating the cruising range when EV travels on a planned route according to drivers demand.
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Mahmudah, Amirotul M. H., A. Budiarto, and S. J. Legowo. "Travel Time Estimation Based on Spot Speed with Instantaneous and Time Slice Model." Applied Mechanics and Materials 776 (July 2015): 80–86. http://dx.doi.org/10.4028/www.scientific.net/amm.776.80.
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In off-line applications, travel time is the main parameter of road performance which can be the main consideration for evaluation and planning of transportation policy, and also to assess the accuracy of transportation modeling. While in on-line application travel time is main information for road users to define their travel behavior. Due to the important of travel time, therefore accurate estimation/prediction of travel time is essential. In order to fulfill it, this research analyzed the accuracy of Instantaneous and Time Slice model, and also evaluate the validity of Time mean speed and Space mean speed in mixed traffic condition. There is not much difference in travel time estimation error between models. The travel time estimation was larger than the actual travel time by floating car. It was also found that the error occurred on time mean speed are less than the space mean speed.
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Park, Dongjoo, Laurence R. Rilett, Parichart Pattanamekar, and Keechoo Choi. "Estimating Travel Time Summary Statistics of Larger Intervals from Smaller Intervals Without Storing Individual Data." Transportation Research Record: Journal of the Transportation Research Board 1804, no. 1 (January 2002): 39–47. http://dx.doi.org/10.3141/1804-06.
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Historically, real-time intelligent transportation systems data are aggregated into discrete periods, typically of 5 to 10 min duration, and are subsequently used for travel time estimation and forecasting. In a previous study of link and corridor travel time estimation and forecasting by using probe vehicles, it was shown that the optimal aggregation interval size is a function of the traffic condition and the application. It is expected that traffic management centers will continue to collect travel time statistics (e.g., mean and variance) from probe vehicles and archive this data at a minimum time interval. Statistical models are developed for estimating the mean and variance of the link and route or corridor travel time for a larger interval by using only the observed mean travel time and variance for each smaller or basic interval. The proposed models are demonstrated by using travel time data obtained from Houston, Texas, which were collected as part of the automatic vehicle identification system of the Houston TranStar system. It was found that the proposed models for estimating link travel time mean and variance for a larger interval were easy to implement and provided results that had minimal error. The route or corridor travel time mean and variance model had considerable error compared with the link travel time mean and variance models.
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Tian, Daxin, Yong Yuan, Honggang Qi, Yingrong Lu, Yunpeng Wang, Haiying Xia, and Anping He. "A Dynamic Travel Time Estimation Model Based on Connected Vehicles." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/903962.
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With advances in connected vehicle technology, dynamic vehicle route guidance models gradually become indispensable equipment for drivers. Traditional route guidance models are designed to direct a vehicle along the shortest path from the origin to the destination without considering the dynamic traffic information. In this paper a dynamic travel time estimation model is presented which can collect and distribute traffic data based on the connected vehicles. To estimate the real-time travel time more accurately, a road link dynamic dividing algorithm is proposed. The efficiency of the model is confirmed by simulations, and the experiment results prove the effectiveness of the travel time estimation method.
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BI, Song, Zhi-jian WANG, Cun-wu HAN, De-hui SUN, Wei-feng ZHAI, and Zhong-cheng ZHAO. "Estimation of left-turning travel time at traffic intersection." Journal of China Universities of Posts and Telecommunications 20 (August 2013): 10–14. http://dx.doi.org/10.1016/s1005-8885(13)60257-5.
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Lindveld, Charles D. R., Remmelt Thijs, Piet H. L. Bovy, and Nanne J. Van der Zijpp. "Evaluation of Online Travel Time Estimators and Predictors." Transportation Research Record: Journal of the Transportation Research Board 1719, no. 1 (January 2000): 45–53. http://dx.doi.org/10.3141/1719-06.
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Travel time is an important characteristic of traffic conditions in a road network. Up-to-date travel time information is important in dynamic traffic management. Presented are the findings of a recently completed research and evaluation program called DACCORD, regarding the evaluation of tools for online estimation and prediction of travel times by using induction loop detector data. Many methods exist with which to estimate and predict travel time by using induction loop data. Several of these methods were implemented and evaluated in three test sites in France, Italy, and the Netherlands. Both cross-tool and cross-site evaluations have been carried out. Travel time estimators based on induction loop detectors were evaluated against observed travel times and were seen to be reasonably accurate (10 percent to 15 percent root mean square error proportional) across different sites for uncongested to lightly congested traffic conditions. The evaluation period varied by site from 4 to 30 days. Results were seen to diverge at higher congestion levels: at one test site, congestion levels were seen to have a strong negative impact on estimation accuracy; at another test site, accuracy was maintained even in congested conditions.
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Chen, Shiyi, and Yiyong Pan. "A Link Real-time Travel Time Estimation on Speed-time Field Traversing Incorporating LSTM Neural Network." Journal of Physics: Conference Series 2491, no. 1 (April 1, 2023): 012035. http://dx.doi.org/10.1088/1742-6596/2491/1/012035.
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Abstract A travel time estimation method based on Speed-time field traversal including LSTM neural network was proposed to increase the real-time and accuracy of travel time estimation. The node departure speed of the traditional piecewise truncated quadratic speed trajectory model was optimized by the road node arrival speed, considering the impact of road conditions on travel time. The node arrival speed was modeled as time series and predicted in the short-term future by combining the LSTM neural network model to construct a spatiotemporally continuous speed trajectory, to estimate the link’s travel time. The method was tested on an actual road and gave considerably improved estimates and results compared to the original method, using LSTM neural network to predict node departure speed and the technique using node arrival speed. According to experimental results, the proposed method performs well in both smooth and crowded traffic circumstances, serving as a benchmark for precise real-time travel time estimation on urban roads.
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Saw, Krishna, Aathira K. Das, Bhimaji K. Katti, and Gaurang J. Joshi. "Travel Time Estimation Modelling under Heterogeneous Traffic: A Case Study of Urban Traffic Corridor in Surat, India." Periodica Polytechnica Transportation Engineering 47, no. 4 (May 15, 2018): 302–8. http://dx.doi.org/10.3311/pptr.10847.
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Achievement of fast and reliable travel time on urban road network is one of the major objectives for a transport planner against the enormous growth in vehicle population and urban traffic in most of the metropolitan cities in India. Urban arterials or main city corridors are subjected to heavy traffic flow resulting in degradation of traffic quality in terms of vehicular delays and increase in travel time. Since the Indian roadway traffic is characterized by heterogeneity with dominance of 2Ws (Two wheelers) and 3Ws (Auto rickshaw), travel times are varying significantly. With this in background, the present paper focuses on identification of travel time attributes such as heterogeneous traffic, road side friction and corridor intersections for recurrent traffic condition and to develop an appropriate Corridor Travel Time Estimation Model using Multi-Linear Regression (MLR) approach. The model is further subjected to sensitivity analysis with reference to identified attributes to realize the impact of the identified attributes on travel time so as to suggest certain measures for improvement.
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Liu, Wen Ting. "Travel Time Prediction of Road Network Based on Multi-Source Data Fusion." Advanced Materials Research 490-495 (March 2012): 850–54. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.850.
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This paper is concerned with the task of travel time pre-diction of urban roadway. For improving the travel time predication ac-curacy, a travel time predication model based multi-source data fusion is proposed. The prediction procedure is divided into two phases, the estimation phase and the prediction phase The method is combined the historical traffic patterns with real-time traffic data as a linear. The resulting model is tested with realistic traffic data, and is found to perform well.
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Wu, Z., C. Li, Y. Wu, F. Xiao, L. Zhu, and J. Shen. "TRAVEL TIME ESTIMATION USING SPATIO-TEMPORAL INDEX BASED ON CASSANDRA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 235–42. http://dx.doi.org/10.5194/isprs-annals-iv-4-235-2018.
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<p><strong>Abstract.</strong> Travel time estimation plays an important role in traffic monitoring and route planning. Taxicabs equipped with Global Positioning System (GPS) devices have been frequently used to monitor the traffic state, and GPS trajectories of taxicabs also used to estimate path travel time in an urban area. However, in most cases, it is difficult to find a trajectory that fits perfectly with the query path, as some road segments may be traveled by no taxicab in present time slot. This makes it hard to estimate the travel time of the query path. This paper proposes a framework to estimate the travel time of a path by using the GPS trajectories of taxicabs as well as map data sources. In this framework, the travel time is represented as a series of residence time in cells (one cell is the gird segmentation unit), thus the key issues of the estimation are: finding the local traffic patterns of frequently shared paths from historical data and computing the stay time in cells. There are three major processes in this framework: trajectories preprocessing, establishing the temporal-spatial index and cell-based travel time estimation. Based on the temporal-spatial index, an algorithm is developed that uses similar route patterns, the cell-based travel time over a period of history and road network information to estimate the travel time of a path. This paper uses GPS trajectories of 10,357 taxicabs over a period of one week to evaluate the framework. The results demonstrate that this paper’s method is effective and feasible in city-wide scenarios.</p>
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El Esawey, Mohamed, and Tarek Sayed. "Travel time estimation in urban networks using limited probes data." Canadian Journal of Civil Engineering 38, no. 3 (March 2011): 305–18. http://dx.doi.org/10.1139/l11-001.
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Travel time is a simple and robust network performance measure that is well understood by the public. However, travel time data collection can be costly especially if the analysis area is large. This research proposes a solution to the problem of limited network sensor coverage caused by insufficient sample size of probe vehicles or inadequate numbers of fixed sensors. Within a homogeneous road network, nearby links of similar character are exposed to comparable traffic conditions, and therefore, their travel times are likely to be positively correlated. This correlation can be useful in developing travel time relationships between nearby links so that if data becomes available on a subset of these links, travel times of their neighbours can be estimated. A methodology is proposed to estimate link travel times using available data from neighbouring links. To test the proposed methodology, a case study was undertaken using a VISSIM micro-simulation model of downtown Vancouver. The simulation model was calibrated and validated using field traffic volumes and travel time data. Neighbour links travel time estimation accuracy was assessed using different error measurements and the results were satisfactory. Overall, the results of this research demonstrate the feasibility of using neighbour links data as an additional source of information to estimate travel time, especially in case of limited coverage.
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Kim, Hyungjoo, and Lanhang Ye. "Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data." Applied Sciences 12, no. 13 (June 26, 2022): 6483. http://dx.doi.org/10.3390/app12136483.
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This study develops a novel estimation method under low-frequency probe data using the Bayesian approach. Given the challenges in estimating travel time under low-frequency probe data and prior distribution of the parameters in a traditional Bayesian approach, the proposed algorithm adopts a historical data-based data-driven method according to the characteristics of travel time regularity. Due to the variability of travel times during peak periods, this paper adopts a mixture distribution of travel times in the Bayesian approach rather than traditional single distribution. The Gibbs sampling method with a burn-in period is used to generate a series of sampling sequences from an unknown joint posterior distribution for estimating the posterior distribution of the parameters. The proposed algorithm is tested using traffic data collected from the Korean freeway section from Giheung IC to Dongtan IC. Both MAPE and RMSE of the estimation results show that the proposed method has the smallest deviation from the ground truth travel time compared to the simple mean and moving average methods. Moreover, the proposed Bayesian estimation yields the smallest standard deviation of MAPE for all test days. The credible intervals for estimated travel times show that the proposed method provides good accuracy in estimating travel time reliability.
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Nabizade Gangeraj, Ebrahim, Gholam Ali Behzadi, and Reza Behzad. "Estimation of Origin – Destination Matrix from Traffic Counts Based On Fuzzy Logic." Civil Engineering Journal 3, no. 11 (December 10, 2017): 1166. http://dx.doi.org/10.28991/cej-030946.
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Determining trip demand matrix is among the basic data in transportation planning. This matrix is derived by surveys, interviews with citizens or questionnaires that required time, money and manpower. Thus, in recent years, demand estimation methods based on network information is taken into consideration. In these methods with the information including: volume, travel time, capacity of the links and initial demand matrix it is possible to estimate the demand matrix. In this paper, we removed the additional parameters in previous studies and used a simple solution to estimate the matrix. This paper proposes a Fuzzy-PFE estimation method that allows to improve the estimation performances of PFE estimator. The objective function presented based on the reduction of travel time and travel time of routs in networks is uncertain. The method is developed by fuzzy sets theory and fuzzy programming that seems to be convenient theoretical framework to represent uncertainty in the available data. The new model is the removal of iterative process of origin - destination matrix estimation using travel time and increase convergence of the model for the large-scale and congested networks by applying little changes in the basic model. In this paper we used TRANSCAD Software to determine the shortest path in the network and optimization of objective function is performed by CPLEX.
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Liu, Fengkai, Jianhua Yang, Mu Li, and Kuo Wang. "MCT-TTE: Travel Time Estimation Based on Transformer and Convolution Neural Networks." Scientific Programming 2022 (April 12, 2022): 1–13. http://dx.doi.org/10.1155/2022/3235717.
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In this paper, we propose a new travel time estimation framework based on transformer and convolution neural networks (CNN) to improve the accuracy of travel time estimation. We design a traffic information fusion component, which fuses the GPS trajectory, real road network, and external attributes, to fully consider the influence of road network topological characteristics as well as the traffic temporal characteristics on travel time estimation. Moreover, we provide a multiview CNN transformer component to capture the spatial information of each trajectory point at multiple regional scales. Extensive experiments on Chengdu and Beijing datasets show that the mean absolute percent error (MAPE) of our MCT-TTE is 11.25% and 11.78%, which is competitive with the state-of-the-arts baselines.
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Tamin, Owen, Badrul Ikram, Ahmad Lutfi Amri Ramli, Ervin Gubin Moung, and Christie Chin Pei Yee. "Travel-Time Estimation by Cubic Hermite Curve." Information 13, no. 7 (June 23, 2022): 307. http://dx.doi.org/10.3390/info13070307.
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Travel time is a measure of time taken to travel from one place to another. Global Positioning System (GPS) navigation applications such as Waze and Google Maps are easily accessible presently and allow users to plan a route based on travel time from one place to another. However, these applications can only estimate general travel time based on a vehicle’s total distance and average safe speed without considering route curvature. A parametric cubic curve has shown a potential result in travel-time estimation through geometric properties. In this paper, travel time has been estimated using the curvature value obtained from the Hermite Interpolation curve fitted to each section of the selected road. Design speed is determined from the curvature value, and thus an algorithm for travel-time estimation incorporating initial driving information is developed. The proposed method’s accuracy was compared to the existing method’s accuracy using a real-life driving test. This comparison demonstrated that the proposed method estimates travel time more accurately than Google Maps and Waze. Future study can further improve the estimation by embedding traffic data into the algorithm.
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Li, Yongyi, Ming Zhang, Yixing Ding, Zhenghua Zhou, and Lingyu Xu. "Real-Time Travel Time Prediction Based on Evolving Fuzzy Participatory Learning Model." Journal of Advanced Transportation 2022 (May 12, 2022): 1–9. http://dx.doi.org/10.1155/2022/2578480.
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Urban expressways take on rapid and external transport in the city due to their fast, safe, and large capacity. Implementing intelligent and active traffic control can effectively improve the performance of urban traffic and mitigate the urban traffic congestion problem. Real-time traffic guidance is one critical way of intelligent active traffic control, and travel time is the most important input for real-time traffic guidance. We employed and improved a machine learning method called the evolving fuzzy participatory learning (ePL) model to predict the freeway travel time online in this paper. The ePL model has a promising nonlinear mapping potential, which is well suitable for the traffic prediction. We used generalized recursive least square (GRLS) to improve the estimation accuracy of the model’s parameters. This model is a fuzzy control model. Its output is the forecasting result which is also the fuzzy reasoning result. We tested this model by comparing it to other travel time prediction approaches, with the freeway data from the Caltrans Performance Measurement System. The results from the improved ePL model showed mean absolute error of 5.941 seconds, mean absolute percentage error of 1.316%, and root mean square error of 10.923 s. The performances are better than those of the baseline models including ARIMA and BPN. This model can be used to predict the travel time in the field to be used for active traffic control and traffic guidance.
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Acharya, Himal. "Travel Time Estimation for Pedestrian with GPS Cell Phones as Probes." Kathford Journal of Engineering and Management 1, no. 1 (December 14, 2018): 27–30. http://dx.doi.org/10.3126/kjem.v1i1.22019.
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This paper estimates the travel time for pedestrian in Kist Medical Hospital- Balkumari route using cell phones’ GPS as probes. Using Google Map’s individual timeline, GPS data was traced for this route. Then, Kalman Filter Algorithm is used to estimate the travel time for pedestrian for that week day. Using algorithm result, statistical tool is used to measure the accuracy of travel time in particular origin-destination pair. Kalman filter algorithm is better approach for travel time estimation since the parameters get updated quickly if there is traffic fluctuation. Based on mean travel time, Kalman filter has better travel time estimation of 16.6 min with the help of historical data in compared to Google Map estimation of 18 min irrespective time of day in above origin-destination pair. Real observation is close to estimated travel time which signifies estimated travel time. Here author manages to compare the mean travel time between Kalman filter estimation and Google map data estimation.
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Yang, Ling, Shouxu Jiang, Fusheng Zhang, and Ming Zhao. "Travel Time Estimation by Learning Driving Habits and Traffic Conditions." Journal of Advanced Transportation 2022 (June 28, 2022): 1–17. http://dx.doi.org/10.1155/2022/1308488.
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Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. There are many factors affecting the travel time of a driver on a given trajectory, including the distance, road type, driving habits, traffic congestion, etc. Existing works fail to model the complex relationships of these factors for TTE. To fill this gap, in this paper, we first analyze how these factors work together in determining the travel time. In particular, the travel time depends on the distance and driving speed on each road segment of the trajectory, where the driving speed depends on the driving habits and the environment, including the static factors like the road type (highway or byway) and speed limit and the dynamic factor like the time of the day and congestion. Among these factors, driving habits and traffic conditions (e.g., jam) are the most difficult ones to model. Second, we propose to learn the driving habits of each driver via meta-learning and estimate the conditions based on the current and historical traffic conditions (via recurrent neural networks) of this road and its connected road segments (via graph convolutional neural network). The experimental results on two real taxi trajectory datasets show that our approach outperforms three state-of-the-art methods significantly.
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Fu, Liping, and Laurence R. Rilett. "Real-Time Estimation of Incident Delay in Dynamic and Stochastic Networks." Transportation Research Record: Journal of the Transportation Research Board 1603, no. 1 (January 1997): 99–105. http://dx.doi.org/10.3141/1603-13.
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The ability to predict the link travel times is a necessary requirement for most intelligent transportation systems (ITS) applications such as route guidance systems. In an urban traffic environment, these travel times are dynamic and stochastic and should be modeled as such, especially during incident conditions. In contrast to traditional deterministic incident delay models, the model presented explicitly considers the stochastic attributes of incident duration. This new model can be used for predicting the delay that a vehicle would experience as it travels through nonrecurring congestion brought about by an incident. The model is operational in the sense that it does not require significant data and computational abilities beyond that which is traditionally used and can be used within traffic models or within actual ITS implementations. A mixed discrete and continuous vehicle-delay model is first derived and estimators of the mean and variance of vehicle delay are identified. A sensitivity analysis subsequently is performed, and a method for updating the estimated delay as new information becomes available is provided.
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Jedwanna, Krit, and Saroch Boonsiripant. "Evaluation of Bluetooth Detectors in Travel Time Estimation." Sustainability 14, no. 8 (April 12, 2022): 4591. http://dx.doi.org/10.3390/su14084591.
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With the current popularity of mobile devices with Bluetooth technology, numerous studies have developed methods to analyze the data from such devices to estimate a variety of traffic information, such as travel time, link speed, and origin–destination estimations. However, few studies have comprehensively determined the impact of the penetration rate on the estimated travel time derived from Bluetooth detectors. The objectives of this paper were threefold: (1) to develop a data-processing method to estimate the travel time based on Bluetooth transactional data; (2) to determine the impact of vehicle speeds on Bluetooth detection performance; and (3) to analyze how the Bluetooth penetration rate affected deviations in the estimated travel time. A 28 km toll section in Bangkok, Thailand, was chosen for the study. A number of Bluetooth detectors and microwave radar devices were installed to collect traffic data in October 2020. Five data-processing steps were developed to estimate the travel time. Based on the results, the penetration rate during the day (50 to 90 percent) was higher than during the night (20 to 50 percent). In addition, we found that speed had adverse effects on the MAC address detection capability of the Bluetooth detectors; for speeds greater than 80 km/h, the number of MAC addresses detected decreased. The minimum Bluetooth penetration rate should be at least 1 percent (or 37 vehicles/h) during peak periods and at least 5 percent (or 49 vehicles/h) during the off-peak period.
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Lagos, Felipe, Sebastián Moreno, Wilfredo F. Yushimito, and Tomás Brstilo. "Urban Origin–Destination Travel Time Estimation Using K-Nearest-Neighbor-Based Methods." Mathematics 12, no. 8 (April 20, 2024): 1255. http://dx.doi.org/10.3390/math12081255.
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Improving the estimation of origin–destination (O-D) travel times poses a formidable challenge due to the intricate nature of transportation dynamics. Current deep learning models often require an overwhelming amount of data, both in terms of data points and variables, thereby limiting their applicability. Furthermore, there is a scarcity of models capable of predicting travel times with basic trip information such as origin, destination, and starting time. This paper introduces novel models rooted in the k-nearest neighbor (KNN) algorithm to tackle O-D travel time estimation with limited data. These models represent innovative adaptations of weighted KNN techniques, integrating the haversine distance of neighboring trips and incorporating correction factors to mitigate prediction biases, thereby enhancing the accuracy of travel time estimations for a given trip. Moreover, our models incorporate an adaptive heuristic to partition the time of day, identifying time blocks characterized by similar travel-time observations. These time blocks facilitate a more nuanced understanding of traffic patterns, enabling more precise predictions. To validate the effectiveness of our proposed models, extensive testing was conducted utilizing a comprehensive taxi trip dataset sourced from Santiago, Chile. The results demonstrate substantial improvements over existing state-of-the-art models (e.g., MAPE between 35 to 37% compared to 49 to 60% in other methods), underscoring the efficacy of our approach. Additionally, our models unveil previously unrecognized patterns in city traffic across various time blocks, shedding light on the underlying dynamics of urban mobility.
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Li, Ying Hong, and Zhao Li. "The Research of Travel Time Estimation Based on Vehicle License Plate Auto Recognition System." Applied Mechanics and Materials 644-650 (September 2014): 1324–29. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1324.
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This Paper proposes a quick matching method for vehicle plate data based on NoSQL database technology considering the huge amounts of traffic information, the method uses the basic traffic network information database which built based on vehicle license plate auto recognition system and it improves the effectiveness and applicability of huge data-matching. Besides, the paper fits the travel time by the time priority principle, and estimates the average travel time by the mean estimation and median estimation according to sample capacity. The practical application shows that this method can effectively improve the efficiency of the mass data processing, and obtain travel time information quickly.
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Turner, Shawn M., Timothy J. Lomax, and Herbert S. Levinson. "Measuring and Estimating Congestion Using Travel Time–Based Procedures." Transportation Research Record: Journal of the Transportation Research Board 1564, no. 1 (January 1996): 11–19. http://dx.doi.org/10.1177/0361198196156400102.
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Procedures are presented for measuring and estimating roadway congestion levels. An assessment of users, uses, and audiences indicates a need for congestion measures that are understood by nontechnical audiences, yet are rigorous enough for technical analyses. Travel time–based measurements are deemed most useful for this wide range of needs. Data collected for an NCHRP project were used to identify the number of travel time observations and roadway segments required for reliable estimates of congestion through direct data collection. The data and previous congestion studies were used to develop surrogate procedures that can estimate congestion statistics with readily available traffic count and roadway inventory data. The surrogate estimation procedures were developed to assist agencies when direct data collection is not practical or feasible. Both of these processes—direct measurement and estimation of travel time–related quantities—are important for quantifying congestion.
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Yang, Ling, Shouxu Jiang, and Fusheng Zhang. "Multitask Learning with Graph Neural Network for Travel Time Estimation." Computational Intelligence and Neuroscience 2022 (March 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/6622734.
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Travel time estimation (TTE) is widely applied for ride dispatching, ride-hailing, and route navigation. Even for a given trajectory, the travel time is affected by many spatial-temporal factors, including static ones such as distance, road type, and so on and dynamic ones such as speed, traffic condition, and so on. Challenges of accurate estimation lie in proper representation of these spatial-temporal factors and more importantly capturing the complex relationship among them for TTE. To tackle such challenges, we present a framework based on the fact that the travel time of each road segment is affected by its adjacent segments. It features a graph convolutional neural network and a recurrent neural network for basic TTE for each road segment and a graph attention network for the relation to estimations on the adjacent road segments. Finally, a multitask learning model is proposed for the travel time of the entire given path and that for each road segment. Experimental results on real taxi trajectory datasets of two cities show that the percentage estimation error of the new approach is well controlled at 13.91% and the proposed method outperforms three state-of-the-art methods significantly.
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Jedwanna, Krit, Chuthathip Athan, and Saroch Boonsiripant. "Estimating Toll Road Travel Times Using Segment-Based Data Imputation." Sustainability 15, no. 17 (August 29, 2023): 13042. http://dx.doi.org/10.3390/su151713042.
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Efficient and sustainable transportation is crucial for addressing the environmental and social challenges associated with urban mobility. Accurate estimation of travel time plays a pivotal role in traffic management and trip planning. This study focused on leveraging machine learning models to enhance travel time estimation accuracy on toll roads under diverse traffic conditions. Two models were developed for travel time estimation under a variety of traffic conditions on the Don Muang Tollway, Bangkok, Thailand: a long short-term memory (LSTM) recurrent neural network model and a support vector regression (SVR) model. Missing data were treated using the proposed segment-based data imputation method. Unlike other studies, the effects of missing input data on the travel time model performance were also analyzed. Traffic parameters, such as speed and flow, along with other relevant parameters (time of day, day of the week, holiday indicators, and a missing data indicator), were fed into each model to estimate travel time on each of the four specific routes. The LSTM and SVR results had similar performance levels based on evaluating the all-day pooled data. However, the mean absolute percentage errors were lower for LSTM during peak periods, while SVR performed slightly better during off-peak periods. Additionally, LSTM coped substantially better than SVR with unusual traffic fluctuations. The sensitivity analysis of the missing input data in this study also revealed that the LSTM model was more robust to the high degree of missing data than the SVR model.
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Rajagopal, Balaji Ganesh, Manish Kumar, Pijush Samui, Mosbeh R. Kaloop, and Usama Elrawy Shahdah. "A Hybrid DNN Model for Travel Time Estimation from Spatio-Temporal Features." Sustainability 14, no. 21 (October 28, 2022): 14049. http://dx.doi.org/10.3390/su142114049.
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Due to recent advances in the Vehicular Internet of Things (VIoT), a large volume of traffic trajectory data has been generated. The trajectory data is highly unstructured and pre-processing it is a very cumbersome task, due to the complexity of the traffic data. However, the accuracy of traffic flow learning models depends on the quantity and quality of preprocessed data. Hence, there is a significant gap between the size and quality of benchmarked traffic datasets and the respective learning models. Additionally, generating a custom traffic dataset with required feature points in a constrained environment is very difficult. This research aims to harness the power of the deep learning hybrid model with datasets that have fewer feature points. Therefore, a hybrid deep learning model that extracts the optimal feature points from the existing dataset using a stacked autoencoder is presented. Handcrafted feature points are fed into the hybrid deep neural network to predict the travel path and travel time between two geographic points. The chengdu1 and chengdu2 standard reference datasets are used to realize our hypothesis of the evolution of a hybrid deep neural network with minimal feature points. The hybrid model includes the graph neural networks (GNN) and the residual networks (ResNet) preceded by the stacked autoencoder (SAE). This hybrid model simultaneously learns the temporal and spatial characteristics of the traffic data. Temporal feature points are optimally reduced using Stacked Autoencoder to improve the accuracy of the deep neural network. The proposed GNN + Resnet model performance was compared to models in the literature using root mean square error (RMSE) loss, mean absolute error (MAE) and mean absolute percentile error (MAPE). The proposed model was found to perform better by improving the travel time prediction loss on chengdu1 and chengdu2 datasets. An in-depth comprehension of the proposed GNN + Resnet model for predicting travel time during peak and off-peak periods is also presented. The model’s RMSE loss was improved up to 22.59% for peak hours traffic data and up to 11.05% for off-peak hours traffic data in the chengdu1 dataset.
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Yang, Xia, Rui Ma, Peng Yang, and Xuegang Jeff Ban. "Link Travel Time Estimation in Double-Queue-Based Traffic Models." Promet - Traffic&Transportation 33, no. 3 (May 31, 2021): 387–97. http://dx.doi.org/10.7307/ptt.v33i3.3515.
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Double queue concept has gained its popularity in dynamic user equilibrium (DUE) modeling because it can properly model real traffic dynamics. While directly solving such double-queue-based DUE problems is extremely challenging, an approximation scheme called first-order approximation was proposed to simplify the link travel time estimation of DUE problems in a recent study without evaluating its properties and performance. This paper focuses on directly investigating the First-In-First-Out property and the performance of the first-order approximation in link travel time estimation by designing and modeling dynamic network loading (DNL) on single-line stretch networks. After model formulation, we analyze the First-In-First-Out (FIFO) property of the first-order approximation. Then a series of numerical experiments is conducted to demonstrate the FIFO property of the first-order approximation, and to compare its performance with those using the second-order approximation, a point queue model, and the cumulative inflow and exit flow curves. The numerical results show that the first-order approximation does not guarantee FIFO and also suggest that the second-order approximation is recommended especially when the link exit flow is increasing. The study provides guidance for further study on proposing new methods to better estimate link travel times.
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Vinagre Diaz, Juan Jose, Ana Belen Rodriguez Gonzalez, and Mark Richard Wilby. "Bluetooth Traffic Monitoring Systems for Travel Time Estimation on Freeways." IEEE Transactions on Intelligent Transportation Systems 17, no. 1 (January 2016): 123–32. http://dx.doi.org/10.1109/tits.2015.2459013.
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Zhang, Yongliang, M. N. Smirnova, A. I. Bogdanova, Zuojin Zhu, and N. N. Smirnov. "Travel time estimation by urgent-gentle class traffic flow model." Transportation Research Part B: Methodological 113 (July 2018): 121–42. http://dx.doi.org/10.1016/j.trb.2018.05.010.
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