Choosing the fastest route for urban distribution based on big data of vehicle travel time

2017 ◽  
Author(s):  
Kesheng Tang ◽  
Min Qian ◽  
Limei Duan
Keyword(s):  
Big Data ◽  
Travel Time ◽  
Urban Distribution ◽  
Vehicle Travel Time

scholarly journals Estimation of Travel Time Based on Ensemble Method With Multi-Modality Perspective Urban Big Data

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 24819-24828 ◽  
Author(s):  
Zhiqiang Zou ◽  
Haoyu Yang ◽  
A-Xing Zhu
Keyword(s):  
Big Data ◽  
Travel Time ◽  
Ensemble Method

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.

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.

Comparison of Simulation Modules of TRANSYT and INTEGRATION Models

1996 ◽  
Vol 1566 (1) ◽  
pp. 1-7 ◽  
Author(s):  
Hesham A. Rakha ◽  
Michel W. Van Aerde
Keyword(s):  
Travel Time ◽  
Cycle Length ◽  
Traffic Signals ◽  
Complex Signal ◽  
Dynamic Features ◽  
Total Travel Time ◽  
Vehicle Delay ◽  
The Difference ◽  
Vehicle Travel Time ◽  
Signal Network

The TRANSYT simulation/optimization model serves as an unofficial international standard against which many measure the efficiency of other methods of coordinating networks of traffic signals that operate at a constant and common cycle length. However, dynamics due to traffic rerouting, the simultaneous operation of adjacent traffic signals at different cycle lengths, the effect of queue spillbacks on the capacity of upstream links, and various forms of real-time intersection control cannot be modeled using a static model such as TRANSYT. This has created a unique niche for a more dynamic signal network simulation tool. Before modeling such special dynamic scenarios, there first exists a need to validate the static signal control features of such a model and to determine if its unique dynamic features still permit it to yield credible static results. This study has two objectives. First, it attempts to illustrate the extent to which estimates of vehicle travel time, vehicle delay, and number of vehicle stops are related when a standard static signal network is examined using both TRANSYT and INTEGRATION. Second, it strives to illustrate that the types of more complex signal timing problems, which at present cannot be examined by the TRANSYT model, can be examined using the dynamic features of INTEGRATION. The results are intended to permit a better appreciation of both their differences and similarities and permit a more informed decision as to when and where each model should be used. Also demonstrated is that INTEGRATION simulates traffic-signalized networks in a manner that is consistent with TRANSYT for conditions in which TRANSYT is valid. Specifically, the difference in total travel time and percentage of vehicle stops is within 5 percent. In addition, it is also shown that INTEGRATION can simulate conditions that represent the limitations to the current TRANSYT model, such as degrees of saturation in excess of 95 percent and adjacent signals operating at different cycle length durations. This analysis of the simulation features of TRANSYT and INTEGRATION is intended to be a precursor to a comparison of their respective optimization routines.

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