scholarly journals CPT Model-Based Prediction of the Temporal and Spatial Distributions of Passenger Flow for Urban Rail Transit under Emergency Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Wei Li ◽  
Min Zhou ◽  
Hairong Dong
Keyword(s):  
Travel Behavior ◽  
Choice Behavior ◽  
Cumulative Prospect Theory ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Spatial Distributions ◽  
Passenger Flow ◽  
Route Choice Behavior ◽  
Urban Rail ◽  
Temporal And Spatial

Emergencies have a significant impact on the passenger flow of urban rail transit. It is of great practical significance to accurately predict the urban rail transit passenger flow and carry out research on its temporal and spatial distributions under emergency conditions. Urban rail transit operating units currently use video surveillance information mainly to process emergencies and rarely use computer vision technology to analyze passenger flow information collected. Accordingly, this paper proposes a passenger flow-based temporal and spatial distribution model for urban rail transit emergencies based on the CPT. First, this paper clarifies the categories and classification of urban rail transit emergencies, analyzes the factors affecting passenger route selection, and establishes a generalized travel cost model for passengers under emergencies. Second, this paper establishes a passenger route choice behavior model for urban rail transit based on the cumulative prospect theory. Finally, taking Beijing as an example, this paper analyzes passenger travel behavior under emergencies based on multiple logistic regression models and analyzes the impact of emergencies on rail transit travel behavior. The research results show that the cumulative prospect theory can better describe the route choice behavior of rail transit passengers under emergencies than the existing models, and this model is of great significance for handling urban rail transit emergencies. The model proposed in this paper can provide a theoretical basis for the government and relevant departments to formulate traffic management measures.

Study on Route Choice Behavior of Urban Rail Transit Passengers

2014 ◽  
Vol 587-589 ◽  
pp. 2252-2256
Author(s):  
Sha Sha Liu ◽  
En Jian Yao ◽  
Yong Sheng Zhang ◽  
Ling Lu
Keyword(s):  
Choice Behavior ◽  
Route Choice ◽  
Personal Characteristics ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Comfort Index ◽  
Route Choice Behavior ◽  
Urban Rail ◽  
New Variables ◽  
Vehicle Travel Time

In order to capture spatiotemporal distribution pattern of passenger flow under networked condition, it is necessary to analyze route choice behavior of urban rail transit passengers. First, angular cost value and comfort index are defined to reflect the influence of network structures, route directions and in-vehicle congestion on passengers’ route choice behavior respectively; Then, two route choice models are proposed respectively for peak and off-peak hours, in which new variables including angular cost value, comfort index and personal characteristics, as well as level of service variables (i.e. in-vehicle travel time, number of transfers and transfer time etc. , which are usually found in the base model) are considered. Finally, the models are calibrated with the surveyed data from Guangzhou Metro and compared with each other. The results show that the new variables significantly improve models’ explanatory and predictive abilities on route choice behavior of urban rail transit passengers.

scholarly journals Hybrid Random Regret Minimization and Random Utility Maximization in the Context of Schedule-Based Urban Rail Transit Assignment

2018 ◽  
Vol 2018 ◽  
pp. 1-28 ◽  
Author(s):  
Dewei Li ◽  
Yufang Gao ◽  
Ruoyi Li ◽  
Weiteng Zhou
Keyword(s):  
Utility Maximization ◽  
Choice Behavior ◽  
Route Choice ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Assignment ◽  
Regret Minimization ◽  
Transit Network ◽  
Route Choice Behavior ◽  
Urban Rail

Route choice is one of the most critical passenger behaviors in public transit research. The utility maximization theory is generally used to model passengers’ route choice behavior in a public transit network in previous research. However, researchers have found that passenger behavior is far more complicated than a single utility maximization assumption. Some passengers tend to maximize their utility while others would minimize their regrets. In this paper, a schedule-based transit assignment model based on the hybrid of utility maximization and regret minimization is proposed to study the passenger route choice behavior in an urban rail transit network. Firstly, based on the smart card data, the space-time expanded network in an urban rail transit was constructed. Then, it adapts the utility maximization (RUM) and the regret minimization theory (RRM) to analyze and model the passenger route choice behavior independently. The utility values and the regret values are calculated with the utility and the regret functions. A transit assignment model is established based on a hybrid of the random utility maximization and the random regret minimization (RURM) with two kinds of hybrid rules, namely, attribute level hybrid and decision level hybrid. The models are solved by the method of successive algorithm. Finally, the hybrid assignment models are applied to Beijing urban rail transit network for validation. The result shows that RRM and RUM make no significant difference for OD pairs with only two alternative routes. For those with more than two alternative routes, the performance of RRM and RUM is different. RRM is slightly better than RUM in some of the OD pairs, while for the other OD pairs, the results are opposite. Moreover, it shows that the crowd would only influence the regret value of OD pair with more commuters. We conclude that compared with RUM and RRM, the hybrid model RURM is more general.

scholarly journals Holiday Destination Choice Behavior Analysis Based on AFC Data of Urban Rail Transit

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Chang-jun Cai ◽  
En-jian Yao ◽  
Sha-sha Liu ◽  
Yong-sheng Zhang ◽  
Jun Liu
Keyword(s):  
Behavior Analysis ◽  
Choice Behavior ◽  
Travel Demand ◽  
Choice Model ◽  
Urban Rail Transit ◽  
Destination Choice ◽  
Rail Transit ◽  
Transit System ◽  
Passenger Flow ◽  
Urban Rail

For urban rail transit, the spatial distribution of passenger flow in holiday usually differs from weekdays. Holiday destination choice behavior analysis is the key to analyze passengers’ destination choice preference and then obtain the OD (origin-destination) distribution of passenger flow. This paper aims to propose a holiday destination choice model based on AFC (automatic fare collection) data of urban rail transit system, which is highly expected to provide theoretic support to holiday travel demand analysis for urban rail transit. First, based on Guangzhou Metro AFC data collected on New Year’s day, the characteristics of holiday destination choice behavior for urban rail transit passengers is analyzed. Second, holiday destination choice models based on MNL (Multinomial Logit) structure are established for each New Year’s days respectively, which takes into account some novel explanatory variables (such as attractiveness of destination). Then, the proposed models are calibrated with AFC data from Guangzhou Metro using WESML (weighted exogenous sample maximum likelihood) estimation and compared with the base models in which attractiveness of destination is not considered. The results show that theρ2values are improved by 0.060, 0.045, and 0.040 for January 1, January 2, and January 3, respectively, with the consideration of destination attractiveness.

Passenger Flow Forecast of Urban Rail Transit Based on Support Vector Regression

2013 ◽  
Vol 433-435 ◽  
pp. 612-616 ◽  
Author(s):  
Bin Xia ◽  
Fan Yu Kong ◽  
Song Yuan Xie
Keyword(s):  
Support Vector Regression ◽  
Risk Function ◽  
Urban Rail Transit ◽  
Support Vector ◽  
Rail Transit ◽  
Risk Minimization ◽  
Promising Alternative ◽  
Minimization Principle ◽  
Passenger Flow ◽  
Urban Rail

This study analyses and compares several forecast methods of urban rail transit passenger flow, and indicates the necessity of forecasting short-term passenger flow. Support vector regression is a promising method for the forecast of passenger flow because it uses a risk function consisting of the empirical error and a regularized term which is based on the structural risk minimization principle. In this paper, the prediction model of urban rail transit passenger flow is constructed. Through the comparison with BP neural networks forecast methods, the experimental results show that applying this method in URT passenger flow forecasting is feasible and it provides a promising alternative to passenger flow prediction.

Operation Organization at Urban Rail Transit Station under Large Passenger Flow

2012 ◽  
Vol 253-255 ◽  
pp. 1995-2000
Author(s):  
Qiao Mei Tang ◽  
Li Ping Shen ◽  
Xian Yong Tang
Keyword(s):  
Transport Process ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Common Condition ◽  
Urban Transit ◽  
Passenger Flow ◽  
Operation Process ◽  
Urban Rail ◽  
Transit Station ◽  
Transit Operation

large passenger flow is a common condition of urban transit operation, and the station bears the pressure of large passenger flow directly. This paper analyzes the reason for the appearance of large passenger flow and the characteristics of it, discusses the principles and methods that the station can apply under large passenger flow combined with the passenger’s transport process and the operation process.

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