scholarly journals A Multistage Multiobjective Model for Emergency Evacuation Considering ATIS

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Ming-Hua Zeng ◽  
Ke-Jun Long ◽  
Zi-Wen Ling ◽  
Xi-Yan Huang
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Network Performance ◽  
Programming Model ◽  
Choice Model ◽  
Compliance Rate ◽  
Emergency Evacuation ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Traffic Demand

The impacts of advanced traveler information system’s (ATIS’s) penetration and compliance rates on network performances during hybrid traffic emergency evacuation are investigated in a degraded road network. Before traffic incident a Path-Size Logit (PSL) route choice model is integrated with constraints on the level of service (LOS) of traffic to formulate a bilevel programming model. It aims at minimizing traffic demand in road network which may locally deteriorate the LOS. The lower level is a PSL-stochastic user equilibrium model for multiple classes of users. During the ongoing incident, a multiobjective multiuser-class stochastic optimization model is established with the objectives of maximizing evacuation reliability and minimizing expected network travel time. Furthermore, computations and analyses are completed for five designated scenarios including a method proposed in previous literature. The results show that the evacuation reliability and different kinds of total expected travel time costs regularly increase with emergency traffic’s ATIS compliance rate and decrease with general traffic’s ATIS penetration rate. The research will help improve transport network performance when considering ATIS’s effect on hybrid traffic.

scholarly journals A Modified Network-Wide Road Capacity Reliability Analysis Model for Improving Transportation Sustainability

Algorithms ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 7
Author(s):  
Kui Ji ◽  
Jianxiao Ma
Keyword(s):  
Reliability Analysis ◽  
Travel Time ◽  
Road Network ◽  
Programming Model ◽  
Network Capacity ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Analysis Model ◽  
Step Size ◽  
Level Model

Reserve capacity is the core of reliability analysis based on road network capacity. This article optimizes its bi-level programming model: the upper-level model has added service level coefficient and travel time limit, which realizes the unification of capacity and travel time reliability analysis to a certain extent. Stochastic user equilibrium (SUE) model based on origin has been selected as the lower-level model, which added capacity constraints to optimize distribution results, and allows the evaluation of reliability to be continued. Through the SUE model, the iterative step size of the method of successive averages can be optimized to improve the efficiency of the algorithm. After that, the article designs the algorithm flow of reliability analysis based on road network capacity, and verifies the feasibility of the designed algorithm with an example. Finally, combined with the conclusion of reliability analysis, this article puts forward some effective methods to improve the reliability of the urban road network.

scholarly journals Design of Signal Timing Plan for Urban Signalized Networks including Left Turn Prohibition

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Qinrui Tang ◽  
Bernhard Friedrich
Keyword(s):  
Travel Time ◽  
Treatment Options ◽  
User Equilibrium ◽  
Signal Timing ◽  
Stochastic User Equilibrium ◽  
Left Turn ◽  
Traffic Demand ◽  
Total Travel Time ◽  
Left Turns ◽  
Demand Patterns

Urban road networks may benefit from left turn prohibition at signalized intersections regarding capacity, for particular traffic demand patterns. The objective of this paper is to propose a method for minimizing the total travel time by prohibiting left turns at intersections. With the flows obtained from the stochastic user equilibrium model, we were able to derive the stage generation, stage sequence, cycle length, and the green durations using a stage-based method which can handle the case that stages are sharing movements. The final output is a list of the prohibited left turns in the network and a new signal timing plan for every intersection. The optimal list of prohibited left turns was found using a genetic algorithm, and a combination of several algorithms was employed for the signal timing plan. The results show that left turn prohibition may lead to travel time reduction. Therefore, when designing a signal timing plan, left turn prohibition should be considered on a par with other left turn treatment options.

scholarly journals Resilience-Based Optimization of Postdisaster Restoration Strategy for Road Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Xinhua Mao ◽  
Jibiao Zhou ◽  
Changwei Yuan ◽  
Dan Liu
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Network Performance ◽  
Network Flows ◽  
Programming Model ◽  
Mixed Integer ◽  
Total System ◽  
Network Resilience ◽  
The Road ◽  
Restoration Strategies

This work proposes a framework for the optimization of postdisaster road network restoration strategies from a perspective of resilience. The network performance is evaluated by the total system travel time (TSTT). After the implementation of a postdisaster restoration schedule, the network flows in a certain period of days are on a disequilibrium state; thus, a link-based day-to-day traffic assignment model is employed to compute TSTT and simulate the traffic evolution. Two indicators are developed to assess the road network resilience, i.e., the resilience of performance loss and the resilience of recovery rapidity. The former is calculated based on TSTT, and the latter is computed according to the restoration makespan. Then, we formulate the restoration optimization problem as a resilience-based bi-objective mixed integer programming model aiming to maximize the network resilience. Due to the NP-hardness of the model, a genetic algorithm is developed to solve the model. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The effects of key parameters including the number of work crews, travelers’ sensitivity to travel time, availability of budget, and decision makers’ preference on the values of the two objectives are investigated as well.

The Impact of Traffic Information on Drivers’ Day-to-Day Route Choice Behavior

2011 ◽  
Vol 97-98 ◽  
pp. 925-930
Author(s):  
Shi Xu Liu ◽  
Hong Zhi Guan
Keyword(s):  
Travel Time ◽  
Road Network ◽  
Choice Behavior ◽  
Choice Model ◽  
Route Choice ◽  
User Equilibrium ◽  
Statistical Characteristics ◽  
Traffic Information ◽  
Route Choice Behavior ◽  
The Impact

The influence of different traffic information on drivers’ day-to-day route choice behavior based on microscopic simulation is investigated. Firstly, it is assumed that drivers select routes in terms of drivers’ perceived travel time on routes. Consequently, the route choice model is developed. Then, updating the drivers’ perceived travel time on routes is modeled in three kinds of traffic information conditions respectively, which no information, releasing historical information and releasing predictive information. Finally, by setting a simple road network with two parallel paths, the drivers’ day-to-day route choice is simulated. The statistical characteristics of drivers’ behavior are computed. Considering user equilibrium as a yardstick, the effects of three kinds of traffic information are compared. The results show that the impacts of traffic information on drivers are related to the random level of driver’s route choice and reliance on the information. In addition, the road network cannot reach user equilibrium in three kinds of information. This research results can provide a useful reference for the application of traffic information system.

scholarly journals BI-LEVEL PROGRAMMING MODEL AND ALGORITHMS FOR STOCHASTIC NETWORK WITH ELASTIC DEMAND

Transport ◽  
2015 ◽  
Vol 30 (1) ◽  
pp. 117-128 ◽  
Author(s):  
Xiang Zhang ◽  
Hao Wang ◽  
Wei Wang
Keyword(s):  
Travel Demand ◽  
Programming Model ◽  
Choice Model ◽  
Consumer Surplus ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Elastic Demand ◽  
Stochastic User Equilibrium ◽  
Proposed Model ◽  
Level Model

Based on a state-of-the-art review of the Road Network Design Problem (RNDP), this paper proposes a bi-level programming model for the RNDP as well as algorithms for it. In the lower level of the proposed model, the elastic-demand Stochastic User Equilibrium (SUE) model is adopted to coincide well with characteristics of users behavior, and additionally, the parameter calibration method for the model is developed based on the Logit path choice model. In the upper level of the proposed model, the consumer surplus is maximized to improve the social benefit of a network in consideration of the travel demand, the construction cost, the off-gas emissions and the saturation level. The algorithm for the lower-level model is developed based on the descent iteration method, Dijkstra’s algorithm and linear search technology. A modified Genetic Algorithm (GA) is developed as the algorithm for the whole bi-level model, which takes designed elitist selection operator, adaptive cross operator, mutation operator and niche technology into consideration. The proposed model and algorithms are applied to a numerical example. The results demonstrate the validity and efficiency of the model and algorithms, which shows a bright prospect of the application in RNDP.

scholarly journals Traffic Paradox Under Different Equilibrium Conditions Considering Elastic Demand

2019 ◽  
Vol 31 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Qiang Tu ◽  
Lin Cheng ◽  
Dawei Li ◽  
Jie Ma ◽  
Chao Sun
Keyword(s):  
Traffic Management ◽  
Network Performance ◽  
Transportation Network ◽  
Travel Cost ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Equilibrium Conditions ◽  
Traffic Demand ◽  
Travel Costs ◽  
Cost Sensitivity

Traffic paradox is an important phenomenon which needs attention in transportation network design and traffic management. Previous studies on traffic paradox always examined user equilibrium (UE) or stochastic user equilibrium (SUE) conditions with a fixed traffic demand (FD) and set the travel costs of links as constants under the SUE condition. However, traffic demand is elastic, especially when there are new links added to the network that may induce new traffic demand, and the travel costs of links actually depend on the traffic flows on them. This paper comprehensively investigates the traffic paradox under different equilibrium conditions including the user equilibrium and the stochastic user equilibrium with a fixed and elastic traffic demand. Origin-destination (OD) mean unit travel cost (MUTC) has been chosen as the main index to characterize whether the traffic paradox occurs. The impacts of travelers’ perception errors and travel cost sensitivity on the occurrence of the traffic paradox are also analyzed. The conclusions show that the occurrence of the traffic paradox depends on the traffic demand and equilibrium conditions; higher perception errors of travelers may lead to a better network performance, and a higher travel cost sensitivity will create a reversed traffic paradox. Finally, several appropriate traffic management measures are proposed to avoid the traffic paradox and improve the network performance.

scholarly journals A regret-based route choice model with asymmetric preference in a stochastic network

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401879323
Author(s):  
Lei Zhao ◽  
Hongzhi Guan ◽  
Xinjie Zhang ◽  
Xiongbin Wu
Keyword(s):  
Loss Aversion ◽  
Equilibrium Model ◽  
Choice Model ◽  
User Equilibrium ◽  
Stochastic User Equilibrium ◽  
Regret Minimization ◽  
Route Network ◽  
Travel Decision ◽  
Gains And Losses ◽  
Random Regret Minimization

In this study, a stochastic user equilibrium model on the modified random regret minimization is proposed by incorporating the asymmetric preference for gains and losses to describe its effects on the regret degree of travelers. Travelers are considered to be capable of perceiving the gains and losses of attributes separately when comparing between the alternatives. Compared to the stochastic user equilibrium model on the random regret minimization model, the potential difference of emotion experienced induced by the loss and gain in the equal size is jointly caused by the taste parameter and loss aversion of travelers in the proposed model. And travelers always tend to use the routes with the minimum perceived regret in the travel decision processes. In addition, the variational inequality problem of the stochastic user equilibrium model on the modified random regret minimization model is given, and the characteristics of its solution are discussed. A route-based solution algorithm is used to resolve the problem. Numerical results given by a three-route network show that the loss aversion produces a great impact on travelers’ choice decisions and the model can more flexibly capture the choice behavior than the existing models.

Privacy Implication of Location-Based Service: Multi-Class Stochastic User Equilibrium and Incentive Mechanism

2019 ◽  
Vol 2673 (12) ◽  
pp. 256-265
Author(s):  
Haitao Hu ◽  
Zhanbo Sun ◽  
Runzhe Liu ◽  
Xia Yang
Keyword(s):  
Travel Time ◽  
Incentive Mechanism ◽  
Time Estimation ◽  
User Equilibrium ◽  
Travel Time Estimation ◽  
Stochastic User Equilibrium ◽  
Location Based Service ◽  
Modeling Framework ◽  
Privacy Concerns ◽  
Traffic Guidance

As a tool to assist traffic guidance and improve service quality, location-based service (LBS) platforms such as route navigation apps rely heavily on the collection and analysis of users’ location/trajectory information, which may evoke privacy concerns. Because of such privacy concerns, users may choose not to provide their information. In certain cases, this may lead to the problem of insufficient data for LBS applications (e.g., travel time estimation). To address this issue, the paper develops a modeling framework to quantify the levels of privacy for mixed user groups and proposes an incentive mechanism to encourage users to provide their location/trajectory information. It is assumed that LBS users have smaller travel time perception error but experience some extra privacy costs compared with the non-LBS users. A bi-level optimal incentive model with stochastic user equilibrium and elastic demand is developed to capture the mixed behavior of multi-class network users. The problem is solved using a meta-heuristic approach combined of genetic algorithm, successive average algorithm, and multiple behavior equilibrium assignment algorithm. The results reveal that the modeling framework can capture the mixed behavior of groups with different privacy levels. The proposed incentive mechanism is able to ensure sufficient data, and simultaneously minimize the required incentive.

scholarly journals A Simultaneous Solution for Reserve Capacity Maximization and Delay Minimization Problems in Signalized Road Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Ozgur Baskan ◽  
Huseyin Ceylan ◽  
Cenk Ozan
Keyword(s):  
Bilevel Programming ◽  
Road Network ◽  
Programming Model ◽  
Numerical Test ◽  
User Equilibrium ◽  
Reserve Capacity ◽  
Delay Minimization ◽  
Weighted Sum Method ◽  
Capacity Maximization ◽  
Bilevel Programming Model

In this study, we present a bilevel programming model in which upper level is defined as a biobjective problem and the lower level is considered as a stochastic user equilibrium assignment problem. It is clear that the biobjective problem has two objectives: the first maximizes the reserve capacity whereas the second minimizes performance index of a road network. We use a weighted-sum method to determine the Pareto optimal solutions of the biobjective problem by applying normalization approach for making the objective functions dimensionless. Following, a differential evolution based heuristic solution algorithm is introduced to overcome the problem presented by use of biobjective bilevel programming model. The first numerical test is conducted on two-junction network in order to represent the effect of the weighting on the solution of combined reserve capacity maximization and delay minimization problem. Allsop & Charlesworth’s network, which is a widely preferred road network in the literature, is selected for the second numerical application in order to present the applicability of the proposed model on a medium-sized signalized road network. Results support authorities who should usually make a choice between two conflicting issues, namely, reserve capacity maximization and delay minimization.

Sign in / Sign up

Export Citation Format

Share Document