An agent-based choice model for travel mode and departure time and its case study in Beijing

2016 ◽  
Vol 64 ◽  
pp. 133-147 ◽  
Author(s):  
Mingqiao Zou ◽  
Meng Li ◽  
Xi Lin ◽  
Chenfeng Xiong ◽  
Chao Mao ◽  
...  
Keyword(s):  
Choice Model ◽  
Travel Mode ◽  
Agent Based ◽  
Departure Time

scholarly journals Assessing Overnight Parking Infrastructure Policies for Commercial Vehicles in Cities Using Agent-Based Simulation

2020 ◽  
Vol 12 (7) ◽  
pp. 2673 ◽  
Author(s):  
Raja Gopalakrishnan ◽  
André Romano Alho ◽  
Takanori Sakai ◽  
Yusuke Hara ◽  
Lynette Cheah ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Choice Model ◽  
Critical Element ◽  
Heavy Vehicles ◽  
Negative Externalities ◽  
Commercial Vehicles ◽  
Agent Based ◽  
Urban Freight ◽  
Urban Freight Transport

Urban freight transport is primarily fulfilled by commercial road vehicles. Within cities, overnight parking is a critical element influencing commercial vehicle operations, particularly for heavy vehicles with limited parking options. Providing adequate overnight parking spaces for commercial vehicles tends to be a challenge for urban planners. Inadequate parking supply can result in illegal parking and additional vehicle kilometers traveled, contributing to traffic congestion and air pollution. The lack of tools for evaluating the impacts of changing parking supply is an impediment in developing parking-related solutions that aim to minimize the negative externalities. In this study, we develop an overnight parking choice model for heavy commercial vehicles and integrate it with SimMobility, an agent-based urban simulation platform, demonstrating the potential of this tool for policy evaluation. Using simulations applied to a case study in Singapore, we compare two parking supply scenarios in terms of vehicle kilometers traveled due to changes in the first and last trips of vehicle tours, as well as resulting impacts in traffic flows.

scholarly journals Quantifying Travel Physical Energy Expenditure for Urban Travellers – A Case Study of Beijing

2020 ◽  
Vol 32 (2) ◽  
pp. 219-228
Author(s):  
Xin Hong ◽  
Lingyun Meng ◽  
Jian An
Keyword(s):  
Energy Expenditure ◽  
Public Transportation ◽  
Choice Model ◽  
Mode Choice ◽  
Economic Cost ◽  
Multinomial Logit Model ◽  
Travel Mode ◽  
Physical Energy ◽  
Travel Mode Choice

Travel physical energy expenditure for travellers has impact on travel mode choice behaviour. However, quantitative study on travel physical energy expenditure is rare. In this paper, the concept of travel physical energy expenditure coefficient has been presented. A case study has been carried out of young travellers in Beijing to get the value of physical energy expenditure per unit time under three transport modes, walking, car and public transportation. A series of experiments have been designed and conducted, which consider influence factors including age, gender, travel mode, riding posture, luggage level and crowded level. By analysing the travel data of money, travel time and physical energy expenditure, we determined that the value of travel physical energy expenditure coefficient δ is 0.058 RMB/KJ, which means that travellers can pay 0.058 RMB to reduce 1 KJ physical energy expenditure. Next, a travel mode choice model has been proposed using a multinomial logit model (MNL), considering economic cost, time cost and physical energy cost. Finally, the case study based on OD from Xizhimen to Tiantongyuan in Beijing was conducted. It is verified that it will be in better agreement with the actual travel behaviour when we take the physical energy expenditure for different types of travellers into account.

Agent-Based Microsimulation Approach for Design and Evaluation of Flexible Work Schedules

2015 ◽  
Vol 2537 (1) ◽  
pp. 167-176 ◽  
Author(s):  
Zheng Zhu ◽  
Chenfeng Xiong ◽  
Xiqun Chen ◽  
Xiang He ◽  
Lei Zhang
Keyword(s):  
Choice Model ◽  
Actual Behavior ◽  
Work Schedules ◽  
Individual Choice ◽  
Work Schedule ◽  
Flexible Work ◽  
Agent Based ◽  
Flexible Work Schedule ◽  
Departure Time ◽  
Flexible Work Schedules

The policy of a flexible work schedule has been practiced for years in order to stimulate the redistribution of departure time of commuters. However, its potential influence on travelers’ departure time shifts is infrequently seen in existing studies. This study extended an agent-based positive departure time choice model to gain perspective on travelers’ dynamic reaction toward the flexible work schedule policy. Unlike most rational behavior models, the positive model emphasizes the bounded rationality in people's actual behavior and allows for heterogeneity among travelers. Dynamic traffic assignment is integrated with this proposed model to build up a feedback loop between individual choice (demand side) and network performance (supply side). Scenarios of different percentages of the population with a flexible work schedule are analyzed. It is found that travelers with flexibility in their work schedules tend to depart for work later to avoid peak periods in the morning. The average travel time in the network will decrease by at most 22% when 60% of the travelers have flexible work schedules.

Joint Choice Model for Airport Passengers’ Travel Mode and Departure Time Based on Agent Theory

2020 ◽  
Vol 2674 (6) ◽  
pp. 177-187
Author(s):  
Danwen Bao ◽  
Tianxuan Zhang ◽  
Shijia Tian ◽  
Zhiwei Di
Keyword(s):  
Decision Making ◽  
Public Transportation ◽  
Travel Behavior ◽  
Time Management ◽  
Imperfect Information ◽  
Bayesian Learning ◽  
Choice Model ◽  
Travel Mode ◽  
Agent Theory ◽  
Departure Time

Numerous strategies have been proposed to modify and transform passengers’ travel mode and departure time with the purpose of mitigating landside traffic pressure of airports. A core solution to tackle this problem is to build a travel behavior model so that pertinent predictions about the extent to which passengers shift their patterns of travel can hopefully be obtained. This paper aims at studying the passengers’ behaviors with respect to the travel mode and departure time based on agent theory. What distinguishes this model from traditional utility maximization theory is that it specifically places emphasis on the decision-making process with imperfect information and bounded rationality. Passengers continuously renew their knowledge of time management and their surrounding environment in the duration of the Bayesian learning process. It is evident that decisions about whether to substitute their current travel mode and departure time will be given thoughtful consideration before traveling, in relation to their presumptive gain and cost for searching. When performing additional searches, passengers tend to depend on a range of decision-making conditions to determine the necessity of converting to a new travel pattern. The process of both searching and deciding can be indicated by production (if–then) rules. These rules basically stem from the data gathered from Nanjing Lukou International Airport (NKG). Furthermore, this paper studies and discusses to what extent passengers will change their travel behaviors under variable costs of public transportation. Finally, this paper provides some recommendations on how to formulate appropriate subway fares.

scholarly journals Multi-Criteria, Co-Evolutionary Charging Behavior: An Agent-Based Simulation of Urban Electromobility

2021 ◽  
Vol 12 (1) ◽  
pp. 18
Author(s):  
Lennart Adenaw ◽  
Markus Lienkamp
Keyword(s):  
Urban Areas ◽  
City Planning ◽  
User Behavior ◽  
Transport Sector ◽  
Simulation Framework ◽  
Agent Based Simulation ◽  
Agent Based ◽  
Charging Station ◽  
Spatio Temporal

In order to electrify the transport sector, scores of charging stations are needed to incentivize people to buy electric vehicles. In urban areas with a high charging demand and little space, decision-makers are in need of planning tools that enable them to efficiently allocate financial and organizational resources to the promotion of electromobility. As with many other city planning tasks, simulations foster successful decision-making. This article presents a novel agent-based simulation framework for urban electromobility aimed at the analysis of charging station utilization and user behavior. The approach presented here employs a novel co-evolutionary learning model for adaptive charging behavior. The simulation framework is tested and verified by means of a case study conducted in the city of Munich. The case study shows that the presented approach realistically reproduces charging behavior and spatio-temporal charger utilization.

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