Passenger Route Choice Behavior Research Articles

Understanding passenger route choice behavior under the influence of detailed route information based on smart card data

Understanding passenger route choice behavior under the influence of detailed route information based on smart card data

Sustainable urban mobility: Flexible bus service network design in the post-pandemic era

The excessive traffic congestion in vehicles lowers the service quality of urban bus system, reduces the social distance of bus passengers, and thus, increases the spread speed of epidemics, such as coronavirus disease. In the post-pandemic era, it is one of the main concerns for the transportation agency to provide a sustainable urban bus service to balance the travel convenience in accessibility and the travel safety in social distance for bus passengers, which essentially reduces the in-vehicle passenger congestion or smooths the boarding–alighting unbalance of passengers. Incorporating the route choice behavior of passengers, this paper proposes a sustainable service network design strategy by selecting one subset of the stops to maximize the total passenger-distance (person × kilometers) with exogenously given loading factor and stop-spacing level, which can be captured by constrained non-linear programming model. The loading factor directly determines the in-vehicle social distance, and the stop-spacing level can efficiently reduce the ridership with short journey distance. Therefore, the sustainable service network design can be used to help the government minimize the spread of the virus while guaranteeing the service quality of transport patterns in the post-pandemic era. A real-world case study is adopted to illustrate the validity of the proposed scheme and model.

Unveiling route choice strategy heterogeneity from smart card data in a large-scale public transport network

We contribute to the understanding of public transport passenger route choice behavior by developing and applying methods that capture behavioral strategies by making use of smart card data. We begin by proposing the classification of possible route choice behavioral strategies in two groups: disaggregated strategies and aggregated strategies. In the former, the alternatives correspond to itineraries, which are fixed sequences of stops and public transport lines. In the latter, common line alternatives are considered, which are combinations of itineraries defined under given criteria. Almost all route choice models use consideration sets composed only of itineraries, while public transport assignment models for strategic analysis mostly use a version of the common lines approach. We postulate that this dichotomy is inappropriate and that, instead, heterogeneity exists in the route choice strategy, both between users and across contexts. With the aim of verifying this hypothesis, we first propose an indicator function constructed as the difference between expected and observed trips for a given behavioral assumption. We apply then the indicator to a case study based on smart card data from the city of Santiago, Chile, from which we find evidence of heterogeneity. We identify individuals that follow either an aggregated or a disaggregated strategy, as well as others who seem to be using a combination of both strategies. We further analyze the heterogeneity hypothesis using an integrated discrete choice and latent class approach, which we apply to the same case study. This approach involves estimating path-size logit models built with alternatives from disaggregated and aggregated strategies, as well as a latent class model built from a combination of both. It also addresses methodological challenges related to the definition of the consideration set and the correction of endogeneity. Results confirm the heterogeneity hypothesis, suggesting that the mean probability that passengers belong to the class that uses a disaggregated strategy for route choice is 51.2%, and that this heterogeneity markedly occurs between individuals, not within their choices. We also find that the latent class considering aggregated alternatives appear to prefer bus over metro, while the latent class considering disaggregated alternatives prefer metro over bus. The fact that waiting time is relatively more burdensome for the aggregated strategy class is in line with this class’s preference for common lines. Walking time and bus crowding are more burdensome for the disaggregated strategy class, in line with their observed modal preferences.

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

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.

A review on route choice behavior and volume control of passengers in urban rail transit network

The Urban Rail Transit (URT) network possesses the features of multi-route and big volume of passengers. To study the route choice behavior and volume control of passengers is a bridge to match the capacity supply and passenger demand in URT network. It’s also a crucial problem proposed by the networked operation of URT. We analyze systematically the effect factors of passengers’ route choice behavior and related research on passenger volume control in URT network, as well as the updated representation of URT network and nature of passenger demand. Then we set up the integrated calculation formula for general travel cost in URT network. afterwards, we summarize the classical research method for route choice behavior, including its main study procedure, the search and identify method for effective routes, logit model, probability distribution model, multi-agent simulation and calculation of the match probability by using big data e.g. AFC data. Importantly, we propose the framework for joint comprehensive prediction of passenger route choice behavior and volume control in URT network based on big data, which displays the mind map of passenger-flow-based prediction control and intelligent decision for future study. This review has great theoretical and practical meanings in improving the service efficiency and quality of URT, so as to balance the load of each line and station in URT network, as well as to reduce or eliminate the passenger waiting time for those being left behind because of congestion.

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

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.

Learning the route choice behavior of subway passengers from AFC data

This paper learns the route choice behavior of passengers from Auto Fare Collection, timetable, and train loading data using a method combined with Bayesian inference and Metropolis-Hasting sampling. First, the influential factors of route choice such as in-vehicle travel time, transfer time, and in-vehicle crowding are given. Next, formulations are established based on AFC, timetable and train loading data, which are merged into a logit model of route choice behavior of subway passengers. Next, an algorithm integrating Bayesian inference and Metropolis-Hasting sampling is designed to calibrate parameters of the logit model. Finally, a case study of Beijing subway is applied to verify the validity of the model and algorithm. A detailed discussion shows that in-vehicle crowding plays a crucial role in passenger route choice behavior.

An Enhanced Dynamic User Optimal Passenger Flow Assignment Model for Metro Networks

By considering the difference between a car driver’s route choice behavior on the road and a passenger’s route choice behavior in urban rail transit (URT), this paper proposes an enhanced Dynamic User Optimal (DUO) passenger flow assignment model for metro networks. To capture realistic URT phenomena, the model has integrated the train operation disturbance constraint. Real passenger and train data are used to verify the proposed model and algorithm. The results indicate that the DUO-based model is more suitable for describing passenger route choice behavior under uncertain conditions compared to a static model. Moreover, this paper found that passengers under oversaturated conditions are more sensitive to train operation disturbances compared to undersaturated passengers.

Shuttle Planning for Link Closures in Urban Public Transport Networks

Urban public transport systems must periodically close certain links for maintenance, which can have significant effects on the service provided to passengers. In practice, the effects of closures are mitigated by replacing the closed links with a simple shuttle service. However, alternative shuttle services could reduce inconvenience at a lower operating cost. This paper proposes a model to select shuttle lines and frequencies under budget constraints. We propose a new formulation that allows a minimal frequency restriction on any line that is operated and minimizes passenger inconvenience cost, which includes transfers and frequency-dependent waiting time costs. This model is applied to a shuttle design problem based on a real-world case study of the Massachusetts Bay Transportation Authority network of Boston, Massachusetts. The results show that additional shuttle routes can reduce passenger delay compared to the standard industry practice, while also distributing delay more equally over passengers, at the same operating budget. The results are robust under different assumptions about passenger route choice behavior. Computational experiments show that the proposed formulation, coupled with a preprocessing step, can be solved faster than prior formulations.

Passenger route choice behavior on transit network with real-time information at stops

Passenger route choice behavior on transit network with real-time information at stops

Transit assignment based on stochastic user equilibrium with passengers perception consideration

Concerning the special nature of the transit network,the generalized path concept that maybe easily describe passenger route choice behavior was put forward.The key cost of each path was considered.Based on the analytical framework of cumulative prospect theory and passengers' perception,a stochastic user equilibrium assignment model was developed.A simple example revealed that the limitations of the traditional method can be effectively improved by this proposed method.The basic assumption of complete rationality in traditional model was improved.It helped us enhance our understanding of the complexity of urban public transportation behavior and the rule of decision-making.The facility layout and planning of the public transportation can be determined with this result,as well as the evaluation of the level of service.In addition,it can also be used as valid data support for traffic guidance.

Passenger Route Choice Behavior with Congestion Consideration

Few studies have adequately focused on passenger route choice behavior with congestion consideration, or provided useful guidance on passenger route choice and hence the transit assignment model, which is the writing motivation of this paper. With congestion consideration, travel cost is assessed and and ways to reduce it also identified. Finally, an actual transit network of Chengdu is used as a case study to demonstrate the benefits of the proposed model. The result indicates that the vehicle capacity is an important factor that cant be ignored and a better understanding of passenger route behavior could significantly benefit public transit system.

Passenger route choice model and algorithm in the urban rail transit network

Purpose: There are several routes between some OD pairs in the urban rail transit network. In order to carry out the fare allocating, operators use some models to estimate which route the passengers choose, but there are some errors between estimation results and actual choices results. The aim of this study is analyzing the passenger route choice behavior in detail based on passenger classification and improving the models to make the results more in line with the actual situations. Design/methodology/approach: In this paper, the passengers were divided into familiar type and strange type. Firstly passenger integrated travel impedance functions of two types were established respectively, after that a multi-route distribution model was used to get the initial route assignment results, then a ratio correction method was used to correct the results taking into account the transfer times, crowd and demand for seats. Finally, a case study for the Beijing local rail transit network is shown. Findings: The numerical example showed that it is logical to take passenger classification and the model and algorithm is effective, the final route choice results are more comprehensive and realistic. Originality/value: The paper offers an improved model and algorithm based on passenger classification for passenger route choice in the urban rail transit network.

The Reliability-Based Stochastic Transit Assignment Problem with Elastic Demand

This paper examines the reliability-based stochastic transit assignment problem with elastic demand. A Variational Inequality (VI) model for this problem is developed. The VI model considers capacity, waiting time and in-vehicle travel time as stochastic variables, and includes Spiess and Florian’s (1989) and de Cea and Fernandez’s (1993) models as special cases. A reliability-based stochastic user equilibrium condition is defined to capture the route choice behavior of passengers. To illustrate the properties of the VI model, numerical studies were conducted on de Cea and Fernandez’s (1993) network. The studies also show that Spiess and Florian’s and de Cea and Fernandez’s models can overestimate the system performance substantially.

Rail Transit Travel Time Reliability and Estimation of Passenger Route Choice Behavior

Applications of automatic fare collection data were investigated, with a focus on analysis of travel time reliability and estimation of passenger route choice behavior. Beijing Metro was used as a case study. A rail journey was decomposed, and each component was studied with regard to the uncertainties involved. Methods were then designed and validated to infer platform elapsed time (PET) for through stations and platform elapsed time–transfer (PET-Trans) for transfer stations by using smart card transactional data, train schedules, and complementary manual surveys. With this information, the journey time distribution of any path can be established, and methods were proposed for inferring route choice proportions. After data preparation, the methods were applied to two typical origins and destinations from the Beijing Metro. Key values concerning travel time reliability, such as PET, PET-Trans, travelers left behind (unable to board), and path coefficients, were obtained and interpreted in detail. The outcome of this research could facilitate analysis of transit service reliability and passenger flow assignment in daily operations.

Probit-Type Reliability-Based Transit Network Assignment

This paper proposes a new probit-type reliability-based transit assignment model in a congested network with unreliable transit services. A new disutility function is developed to model the passenger route choice behavior under unreliable conditions; passengers will make a trade-off between the transit service reliability and the expected travel time for their route choice. In the proposed model, the total transit passenger demand is fixed, but the in-vehicle travel times by transit routes are formulated stochastically because of unreliable traffic conditions, particularly for buses on the road network. The effects of congestion on transit networks, such as elastic transit line frequencies and passenger overload delays, are also considered in the proposed model. A numerical example is provided to illustrate the applications of the proposed model and solution algorithm. It is shown that the traditional probit-type stochastic user equilibrium transit assignment is a special case of the proposed model. The findings also reveal the distribution of passenger waiting times and their overload delays at congested transit stations.

Probit-Type Reliability-Based Transit Network Assignment

This paper proposes a new probit-type reliability-based transit assignment model in a congested network with unreliable transit services. A new disutility function is developed to model the passenger route choice behavior under unreliable conditions; passengers will make a trade-off between the transit service reliability and the expected travel time for their route choice. In the proposed model, the total transit passenger demand is fixed, but the in-vehicle travel times by transit routes are formulated stochastically because of unreliable traffic conditions, particularly for buses on the road network. The effects of congestion on transit networks, such as elastic transit line frequencies and passenger overload delays, are also considered in the proposed model. A numerical example is provided to illustrate the applications of the proposed model and solution algorithm. It is shown that the traditional probit-type stochastic user equilibrium transit assignment is a special case of the proposed model. The findings also reveal the distribution of passenger waiting times and their overload delays at congested transit stations.

Influence of Changes in Level on Passenger Route Choice in Railway Stations

In assessing the design of a public transfer station, it is important to be able to predict the routes taken by passengers. Most simulation tools use simple route choice models that take into account only the shortest walking distance or walking time between a passenger's origin and destination. To improve this type of route choice model, other factors affecting passenger route choice need to be identified. Also, the way these factors influence route choice behavior needs to be determined to indicate how each factor is valued. In this research, route choice data have been collected in two Dutch train stations by following passengers through the facility from their origins to their destinations. These data have been used to estimate extended route choice models. The focus is on the influences of level changes in walking routes on passenger route choice behavior. It appears that ways of bridging level changes (ramps, stairs, escalators) each have a significant and different impact on the attractiveness of a route to a traveler.

Influence of Changes in Level on Passenger Route Choice in Railway Stations

In assessing the design of a public transfer station, it is important to be able to predict the routes taken by passengers. Most simulation tools use simple route choice models that take into account only the shortest walking distance or walking time between a passenger's origin and destination. To improve this type of route choice model, other factors affecting passenger route choice need to be identified. Also, the way these factors influence route choice behavior needs to be determined to indicate how each factor is valued. In this research, route choice data have been collected in two Dutch train stations by following passengers through the facility from their origins to their destinations. These data have been used to estimate extended route choice models. The focus is on the influences of level changes in walking routes on passenger route choice behavior. It appears that ways of bridging level changes (ramps, stairs, escalators) each have a significant and different impact on the attractiveness of a...

A Study on the Traveling Time Shortened by Common Tariff System

Introduction of a common tariff system not only makes new demand by fare reduction in transfer but also shortens the traveling time forpresent passengers by providing various options of routes. This paper describes the construction of a dynamic transport network model which displays the condition of mass transit service and route choice behavior of passengers, for calculating the amount of shortening in the traveling time. The result of analysis in the urban network of Kyoto makes sure of shortening the traveling time by introduction of common tariff system, and displays its cause.