scholarly journals Assessing Expected Accuracy of Probe Vehicle Travel Time Reports

1999 ◽  
Vol 125 (6) ◽  
pp. 524-530 ◽  
Author(s):  
Bruce Hellinga ◽  
Liping Fu
Keyword(s):  
Travel Time ◽  
Probe Vehicle ◽  
Vehicle Travel Time

Analyzing Travel Time Reliability from Sparse Probe Vehicle Data: A Case Study on the Effects of Spatial and Temporal Aggregation

2021 ◽  
pp. 036119812110315
Author(s):  
Markus Steinmaßl ◽  
Stefan Kranzinger ◽  
Karl Rehrl
Keyword(s):  
Travel Time ◽  
Time Of Day ◽  
Temporal Aggregation ◽  
Travel Time Reliability ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Depth Analysis ◽  
Data Source ◽  
Travel Time Index

Travel time reliability (TTR) indices have gained considerable attention for evaluating the quality of traffic infrastructure. Whereas TTR measures have been widely explored using data from stationary sensors with high penetration rates, there is a lack of research on calculating TTR from mobile sensors such as probe vehicle data (PVD) which is characterized by low penetration rates. PVD is a relevant data source for analyzing non-highway routes, as they are often not sufficiently covered by stationary sensors. The paper presents a methodology for analyzing TTR on (sub-)urban and rural routes with sparse PVD as the only data source that could be used by road authorities or traffic planners. Especially in the case of sparse data, spatial and temporal aggregations could have great impact, which are investigated on two levels: first, the width of time of day (TOD) intervals and second, the length of road segments. The spatial and temporal aggregation effects on travel time index (TTI) as prominent TTR measure are analyzed within an exemplary case study including three different routes. TTI patterns are calculated from data of one year grouped by different days-of-week (DOW) groups and the TOD. The case study shows that using well-chosen temporal and spatial aggregations, even with sparse PVD, an in-depth analysis of traffic patterns is possible.

scholarly journals A Parallel Meta-Heuristic Approach to Reduce Vehicle Travel Time in Smart Cities

2020 ◽  
Author(s):  
Hector Rico-Garcia ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Hector Migallon-Gomis
Keyword(s):  
Travel Time ◽  
Smart City ◽  
Smart Cities ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Departure Point ◽  
Classic Problem ◽  
Iot Devices ◽  
Vehicle Travel Time ◽  
Gpu Architecture

The development of the smart city concept and the inhabitants’ need to reduce travel time, as well as society’s awareness of the reduction of fuel consumption and respect for the environment, lead to a new approach to the classic problem of the Travelling Salesman Problem (TSP) applied to urban environments. This problem can be formulated as “Given a list of geographic points and the distances between each pair of points, what is the shortest possible route that visits each point and returns to the departure point?” Nowadays, with the development of IoT devices and the high sensoring capabilities, a large amount of data and measurements are available, allowing researchers to model accurately the routes to choose. In this work, the purpose is to give solution to the TSP in smart city environments using a modified version of the metaheuristic optimization algorithm TLBO (Teacher Learner Based Optimization). In addition, to improve performance, the solution is implemented using a parallel GPU architecture, specifically a CUDA implementation.

A Modeling Analysis of Impact from E-Hailing Service on Non-Work Travel Mode in Shanghai, China

2018 ◽  
Vol 2672 (47) ◽  
pp. 125-134 ◽  
Author(s):  
Jiayu Zhong ◽  
Xin Ye ◽  
Ke Wang ◽  
Dongjin Li
Keyword(s):  
Travel Time ◽  
Travel Behavior ◽  
Choice Model ◽  
Mode Choice ◽  
Rapid Development ◽  
Influential Factors ◽  
Travel Mode ◽  
Explanatory Variables ◽  
Bus Travel Time ◽  
Vehicle Travel Time

With the rapid development of mobility services, e-hailing service have been highly prevalent and e-hailing travel has become a part of daily life in many cities in China. At the same time, travelers’ mode choice behaviors have been influenced to some degree by different factors, and in this paper, a web-based retrospective survey initially conducted in Shanghai, China is used to analyze the extent to which various factors are influencing mode choice behaviors. Then, a multinomial-logit-based mode choice model is developed to incorporate the e-hailing auto mode as a new travel mode for non-work trips. The developed model can help to identify influential factors and quantify their impact on mode choice probabilities. The developed model involves a variety of explanatory variables including e-hailing/taxi fare, bus travel time, rail station access/egress distance, trip distance, car in-vehicle travel time as well as travelers’ socioeconomic and demographic characteristics, etc. The model indicates that the e-hailing fare, travel companions and some travelers’ characteristics (e.g., age, income, etc.) are significant factors influencing the choice of e-hailing mode. The alternative-specific constant in the e-hailing utility equation is adjusted to match the observed market share of the e-hailing mode. Based on the developed model, elasticities of LOS attributes are computed and discussed. The research methods used in this paper have the potential to be applied to investigate travel behavior changes under the influence of emerging travel modes. The research findings can aid in evaluating policies to manage e-hailing services and improve their levels of services.

Freight Vehicle Travel Time Analysis: A Case Study of Birgunj Nagdhunga Corridor

2021 ◽  
Vol 08 (02) ◽  
pp. 1-13
Author(s):  
Abhishek Jha ◽  
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
License Plate ◽  
Monitoring Method ◽  
Factors Affecting ◽  
Average Travel Time ◽  
Major Factors ◽  
Vehicle Travel Time ◽  
Freight Vehicles

This study covers the freight vehicle, which clears the custom clearance process for Kathmandu and transports the same goods to Kathmandu from Birgunj. In this study average travel time for freight vehicles from Birgunj to Nagdhunga has been studied, along with the factors affecting the travel time from Birgunj to Nagdhunga. License plate monitoring method of the freight vehicles was done to find the average travel time and a questionnaire survey was done to identify the factors affecting travel time of the freight vehicle. The travel time from Birgunj to Nagdhunga is different for different types of, vehicle and good. The fastest average travel time is of fixed container of 40 feet size with 23.2 hours and longest average time is for fixed container of 20 feet size with 28.95 hours. The average travel time for non-degradable goods is 26.5 hours and for degradable goods is 22.38 hours. Major factors affecting the travel time are traffic congestion along the route, bad road condition along the route and hilly road with sharp bends, turns and grade.

Which Aggregation Fits Best? The Use of Linear Regression to Show the Influence of Temporal and Spatial Aggregation of Sparse Probe Vehicle Data on the Explanation of Travel Time Reliability

2021 ◽  
pp. 036119812110578
Author(s):  
Stefan Kranzinger ◽  
Markus Steinmaßl
Keyword(s):  
Linear Regression ◽  
Travel Time ◽  
Linear Regression Analysis ◽  
Spatial Aggregation ◽  
Travel Time Reliability ◽  
Road Design ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Temporal And Spatial ◽  
Design Characteristics

Aggregation of sparse probe vehicle data (PVD) is a crucial issue in travel time reliability (TTR) analysis. This study, therefore, examines the effect of temporal and spatial aggregation of sparse PVD on the results of a linear regression analysis where two different measures of TTR are analyzed as the dependent variable. Our results show that by aggregating the data to longer time intervals and coarser spatial units the linear model can explain a higher proportion of the variance in TTR. Furthermore, we find that the effects of road design characteristics in particular depend on the variable used to represent TTR. We conclude that the temporal and spatial aggregation of sparse PVD affects the results of linear regression explaining TTR.

A Parallel Metaheuristic Approach to Reduce Vehicle Travel Time for Smart Cities Sustainability

2021 ◽  
pp. 37-49
Author(s):  
Hector Rico-Garcia ◽  
Jose-Luis Sanchez-Romero ◽  
Antonio Jimeno-Morenilla ◽  
Hector Migallon-Gomis
Keyword(s):  
Travel Time ◽  
Smart Cities ◽  
Vehicle Travel Time

Identifying the Determinants of Light Rail Mode Choice for Medium- and Long-Distance Trips

2012 ◽  
Vol 2275 (1) ◽  
pp. 30-38 ◽  
Author(s):  
Lieve Creemers ◽  
Mario Cools ◽  
Hans Tormans ◽  
Pieter-Jan Lateur ◽  
Davy Janssens ◽  
...  
Keyword(s):  
Travel Time ◽  
Transport System ◽  
Public Transport ◽  
Mode Choice ◽  
Light Rail ◽  
Rail Transit ◽  
Light Rail Transit ◽  
Long Distance ◽  
Specific Factors ◽  
Vehicle Travel Time

The introduction of new public transport systems can influence society in a multitude of ways ranging from modal choices and the environment to economic growth. This paper examines the determinants of light rail mode choice for medium- and long-distance trips (10 to 40 km) for a new light rail system in Flanders, Belgium. To investigate these choices, the effects of various transport system–specific factors (i.e., travel cost, in-vehicle travel time, transit punctuality, waiting time, access and egress time, transfers, and availability of seats) as well as the travelers' personal traits were analyzed by using an alternating logistic regression model, which explicitly takes into account the correlated responses for binary data. The data used for the analysis stem from a stated preference survey conducted in Flanders. The modeling results are in line with literature: most transport system–specific factors as well as socioeconomic variables, attitudinal factors, perceptions, and the frequency of using public transport contribute significantly to the preference for light rail transit. In particular, the results indicate that the use of light rail is strongly influenced by travel cost and in-vehicle travel time and to a lesser extent by waiting and access–egress time. Seat availability appeared to play a more important role than did transfers in deciding to choose light rail transit. The findings of this paper can be used by policy makers as a frame of reference to make light rail transit more successful.

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