A Model for Urban Distribution System under Disruptions of Vehicle Travel Time Delay

2009 ◽  
Author(s):  
Zheng Wang ◽  
Jia Shi
Keyword(s):  
Time Delay ◽  
Travel Time ◽  
Distribution System ◽  
Urban Distribution ◽  
Vehicle Travel Time

scholarly journals Travel Time Delay at Pedestrian Crossings Based on Microsimulations

2014 ◽  
Vol 58 (1) ◽  
pp. 47 ◽  
Author(s):  
István Fi ◽  
Zsuzsanna Kovács Igazvölgyi
Keyword(s):  
Time Delay ◽  
Travel Time

scholarly journals Modeling the City Distribution System Reliability with Bayesian Networks to Identify Influence Factors

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Hao Zhang ◽  
Liyu Zhu ◽  
Shensi Xu
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
System Reliability ◽  
Distribution System ◽  
Complex Problem ◽  
Practical Significance ◽  
Correlation Degree ◽  
Urban Distribution ◽  
Distribution System Reliability ◽  
The City

Under the increasingly uncertain economic environment, the research on the reliability of urban distribution system has great practical significance for the integration of logistics and supply chain resources. This paper summarizes the factors that affect the city logistics distribution system. Starting from the research of factors that influence the reliability of city distribution system, further construction of city distribution system reliability influence model is built based on Bayesian networks. The complex problem is simplified by using the sub-Bayesian network, and an example is analyzed. In the calculation process, we combined the traditional Bayesian algorithm and the Expectation Maximization (EM) algorithm, which made the Bayesian model able to lay a more accurate foundation. The results show that the Bayesian network can accurately reflect the dynamic relationship among the factors affecting the reliability of urban distribution system. Moreover, by changing the prior probability of the node of the cause, the correlation degree between the variables that affect the successful distribution can be calculated. The results have significant practical significance on improving the quality of distribution, the level of distribution, and the efficiency of enterprises.

scholarly journals Estimates of Economic Cost of Congestion Travel Time Delay Between Onne-Seaport and Eleme-Junction Traffic Corridor

2020 ◽  
Vol 11 (2) ◽  
pp. 33-43
Author(s):  
Theophilus C. Nwokedi ◽  
Lazarus I. Okoroji ◽  
Ifiok Okonko ◽  
Obed C. Ndikom
Keyword(s):  
Time Delay ◽  
Travel Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Oil And Gas ◽  
Economic Cost ◽  
Oil And Gas Industry ◽  
Research Approach ◽  
Traffic Corridor ◽  
Output Losses

AbstractTravelers along the Onne-seaport to Eleme-junction road corridor in the hub of the oil and gas industry in Port-Harcourt, Nigeria, have continued to experience very serious traffic congestion travel time delays, culminating into loss of man-hours and declining productivity. This study estimated the economic cost of traffic congestion travel time delay along the corridor, with a view to providing economic justification for developing traffic management policies and road infrastructure, to remedy it. A mixed research approach was adopted in which data was sourced through field survey and from secondary sources. The gross output model was used to estimate the output losses occasioned by productive time losses related to traffic congestion. The study established that the average daily traffic congestion travel time delay along the traffic corridor by travelers in trucks, car, bus and taxi modes are 104.17 minutes, 46.60 minutes, 58.5 minutes and 56.4 minutes respectively. The estimated daily aggregate economic cost of output losses associated with traffic congestion time delay on the corridor is 46049809.8 naira (210923.5USD) for all modes. This justifies any investment in traffic congestion remedial strategies along the route.

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.

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