scholarly journals Multiple Equilibrium Behaviors considering Human Exposure to Vehicular Emissions

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Zhanbo Sun ◽  
Yu Tan ◽  
Rui Ma ◽  
Xia Yang ◽  
Jie Zhang
Keyword(s):  
Travel Time ◽  
Human Exposure ◽  
Equilibrium Problems ◽  
Urban Transportation ◽  
Multiple Equilibrium ◽  
Vehicular Emissions ◽  
Time Component ◽  
Diagonalization Method ◽  
System Improvement ◽  
Pareto Improving

Emissions produced by urban transportation activities are harmful to people’s health and they also affect people’s trip-making decisions. In this paper, we explore the multiple equilibrium behaviors considering human exposure to vehicular emissions. We assume that a portion of transportation users are environmental advocates and their route decisions are based on some composite cost functions comprise of a travel time component and an emission exposure component. We then study the multiple equilibrium behaviors with multiple types of users on a traffic network. The multiple equilibrium problems are further converted into variational inequality (VI) problems and they are solved using a method of successive average- (MSA-) based diagonalization method. Per the specific network setting, we find that as travelers become more concerned about their exposure to vehicular emissions, the system emission exposure, travel time, and the total cost get reduced; i.e., Pareto improving solutions are achieved. By analyzing the multiple equilibrium behaviors, we find that the system gets better if more users become environmental advocates. And the change of a small percentage of users should already lead to a good system improvement.

scholarly journals Let Me Save You Some Time... On Valuing Travelers’ Time in Urban Transportation

2019 ◽  
Vol 20 (2) ◽  
pp. 206-229
Author(s):  
Maria Nordström ◽  
Sven Ove Hansson ◽  
Muriel Beser Hugosson ◽  
Keyword(s):  
Travel Time ◽  
Urban Transportation ◽  
Urban Transport ◽  
Economic Resource ◽  
Underlying Assumption ◽  
Economic Interpretation ◽  
Travel Time Savings ◽  
Nature Of Time ◽  
Infrastructure Investments ◽  
Basic Features

Systems of urban transportation are largely shaped through planning practices. In transport economics, the benefits of infrastructure investments consist mainly of travel time savings calculated using monetary values of time. The economic interpretation of the value of travel time has significantly shaped our urban environment and transportation schemes. However, there is often an underlying assumption of transferability between time and money, which arguably does not sufficiently take into account the specific features of time. In this paper, we analyze the various properties of time as an economic resource using findings in behavioral economics and psychology. Due to limitations in the standard model, it is proposed that an alternative model value should be investigated in which time rather than money is the primary carrier of and the basic features of such a model are outlined. An improved understanding the nature of time as a source of utility puts us in a better position to determine what aspects of time matter. Additionally, the analysis can be applied to further develop modeling where value of time plays a significant role; such as new models for the planning of urban transport.

scholarly journals The Vehicles Traffic Flow Optimization in an Urban Transportation System by Using Simulation Modeling

2017 ◽  
Vol 22 (3) ◽  
pp. 190-197
Author(s):  
Marin-Marian Coman ◽  
Dorel Badea
Keyword(s):  
Travel Time ◽  
Simulation Modeling ◽  
Emergency Services ◽  
Road Traffic ◽  
Urban Transportation ◽  
Transportation System ◽  
Time Duration ◽  
The Road ◽  
Modeling Software ◽  
On The Road

Abstract The urban transportation system is characterized by the urban roads development and growth of road traffic, which leads, most of the time, to a series of congestions in the vehicles traffic. Consequently, due to a high time duration spent on the road traffic, the travel time from a location to another one could be very upsetting for any car drivers, or embarrassing for emergency services and vehicle convoys that carry goods or sensitive items. Those are mainly reasons for using simulation modeling to analyze and optimize the travel time of the road traffic actors in the an urban transportation system. This paper focuses on optimization vehicles flow in a crowded area of Sibiu city, by using agent-based modeling concept and AnyLogic simulation modeling software.

A Heuristic Algorithm to Study the Influence of Road Preference on Network Capacity

2013 ◽  
Vol 694-697 ◽  
pp. 3361-3365 ◽  
Author(s):  
Wei Dong Yang ◽  
Dong Tao Fan ◽  
Hua Bai
Keyword(s):  
Travel Time ◽  
Discrete Choice ◽  
Heuristic Algorithm ◽  
Choice Model ◽  
Urban Transportation ◽  
Heuristic Method ◽  
Network Capacity ◽  
Preference Modeling ◽  
The Road ◽  
Time Modeling

In this study, an algorithm was proposed to study the influence of road preference on the network capacity in urban transportation system. Of particular interest is to model the discrete choice model with respect to the road preference and the travel time. New formulations are presented in terms of the multi-objective road preference modeling and the travel time modeling for multi vehicle types. Compared with existing techniques, an improved heuristic method is put forward to explore the network capacity.

Urban Velocity Fields

1970 ◽  
Vol 2 (2) ◽  
pp. 211-224 ◽  
Author(s):  
S. Angel ◽  
G.M. Hyman
Keyword(s):  
Travel Time ◽  
Urban Transportation ◽  
Transportation System ◽  
Velocity Fields ◽  
Minimum Time ◽  
Straight Line ◽  
The City ◽  
High Congestion

The city is not an undifferentiated terrain and travel does not occur along straight-line paths at constant velocities. Variations in travel velocities at different locations bend the minimum time paths away from regions of high congestion. This paper discusses a transformation of the urban plane into a time surface on which distance corresponds to travel time, and describes the construction of minimum paths and isochrones for various velocity fields. This view of the urban transportation system allows us to discover some of the important features which are often hidden in a network description of the system.

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