Travel Choice Behavior Research Articles

Activity-based and agent-based transport model of Melbourne: an open multi-modal transport simulation model for Greater Melbourne

Activity- and agent-based models for simulating transport systems have attracted significant attention in recent years. However, building these types of models at a city-wide level and including motorized (i.e. cars and public transport) and non-motorized (i.e. walk and bicycle) modes of transport is a complicated and involved task. This paper presents an open workflow for creating large-scale multi-modal agent-based transport simulation models. The workflow brings together a number of external tools, for example, an activity-based demand generation tool and a road network generation tool, and a set of tools developed for the agent-based model parameter estimation, calibration, and simulation post-processing. We used this workflow to create an activity- and agent-based model for Melbourne and compared the output of the simulation model with observations from the real world in terms of mode share, road volume, travel time, and travel distance. Through these comparisons, we showed that our model is suitable for studying mode choice and road usage behavior of travelers. The calibrated model could be used to test road network change interventions. In addition, a similar workflow can be applied for building simulation models for other cities or could be expanded to include more complicated travel behaviors.

Optimization of ticket pricing for high-speed railway considering full competitive environment

The rapid development of high-speed railways (HSR) worldwide has provided a fast, convenient, safe, and comfortable mode of transportation. It is also an important public transportation tool for green travel, contributing to the reduction of carbon emissions. HSR has a strong competitive advantage in long-distance passenger transportation, but it faces significant competition from the aviation industry. Airlines attract passengers with discounted fares and differentiated pricing strategies, apart from shorter travel times. Therefore, it is necessary for railway enterprises to enhance their competitiveness through differentiated pricing methods for HSR. In this study, we focus on optimizing a long-distance origin-destination (OD) pair and creating a competitive market environment by considering all operating trains and flights for the selected OD pair. We aim to improve the realism of the study by considering passengers’ fully rational travel choice behavior. To reduce the complexity of variables, passengers are grouped based on their expected departure time and income level. A mixed integer linear programing model is developed to address the problem, aiming to minimize overall travel costs for passengers while ensuring that the income of the railway enterprise does not fall below a specified value. By setting this specific value, the maximum revenue of HSR train tickets can be obtained.

Exploring hierarchy shift of travelers’ public transport dependence using an improved Apriori algorithm

ABSTRACT Special external environments will lead to significant changes in the use behavior and dependence degree of different PT travellers, but it is difficult to analyze the mechanism of the hierarchy shift of travelers’ public transportation (PT) dependence. Exploring travelers’ dependence on PT is conducive to understanding individuals’ travel choice behavior and optimizing PT operation organizations. Developing methods for analyzing the internal causal relationship between travelers’ dependence on PT and the key influencing factors under the special condition is an issue. Therefore, the individual travel chains are constructed by associating and matching the multisource PT big data and travel survey data. Thereafter, the K-means algorithm and an improved Apriori algorithm are developed to mine the frequent association rules of groups, and a framework of cross-hierarchy policy implications is derived based on the differences in association rules. Finally, the stated preference survey method is used to measure the effectiveness of the policies.

Analysis of Travel Choice Behaviours of Commuters in the Niger Delta Region of Nigeria

This study modelled the travel choice behaviour among commuters in a few selected coastal capital cities in the Niger Delta, reference to road and inland waterways. Multinomial-logit model was used in explaining discrete choiceswhile multiple Regressions was used for the model equation. The following factors that influence the behaviour of commuters such as travel time, cost, and socio-demographic and socio-economic characteristics were investigated. A simple severity index was also used to ascertain the safety level of both ferries and automobiles which could influence commuters' mode choice behaviours. The alternative transport modes available to users were considered and include –private cars, walking, bus service and ferry (boat). From the findings, the use of private cars will be reduced considerably if the public transport sector is given attention in terms of infrastructure/facilities so they can compete. The greater percent that refused to change from private cars to public monorail show that private car users are attached to their cars not just for convenience but for prestige, flexibility, and privacy and finally, it was gathered from the model equation that variables like employment status, distance to work and driver's license had zero effect on the behaviour of people per their choice of transport mode.

A review and outlook of machine learning-based travel choice behavior research

A review and outlook of machine learning-based travel choice behavior research

Study on Induced Passenger Flow Forecast for Intercity High-Speed Rail

To forecast the induced passenger flow of intercity high-speed rail, two related multinomial logit models are applied to describe travelers’ travel choice behavior under changes in the travel environment. The first model describes different choices of travel mode by travelers between origin–destination pairs, while the second model describes the choice of travel frequency by different travelers. By introducing the consumption surplus variable of travelers, the two selected models are correlated and the model is calibrated using data from a behavioral survey and an intention survey. The results show that the polynomial logit model is applicable for forecasting induced passenger flow after changes in the travel environment. The consumption surplus of travelers is the key factor influencing changes in travel frequency, and the social characteristics of travelers and the economic development features of origin and destination cities have significant impacts on travel frequency. In-transit time is the key factor affecting the demand elasticity of travel frequency. The range of demand elasticity for short-distance business and non-business travel is between −0.61 and −0.79. The results of the demand elasticity analysis were applied to predict that the travel frequency of people from Nanjing to Huai’an, Suqian, Lianyungang, and Yancheng will increase by about 45% after the opening of the Nanjing-Huai’an intercity high-speed railway.

Investigating the Impacts of Urban–Rural Bus Service Quality on Rural Residents’ Travel Choices Using an SEM–MNL Integration Model

The high-quality development of urban–rural buses is an important way to narrow the gap between urban and rural areas. However, studies on the quality of bus services and its impact on bus travel-mode choice have mostly focused on urban buses, with less attention paid to urban–rural buses. This paper aims to explore how urban–rural bus service quality affects rural residents’ travel-mode-choice behavior based on survey data from rural residents in Henan Province, China. A structural equation model (SEM) is applied to examine the critical factors affecting rural residents’ satisfaction with urban–rural bus service quality and the heterogeneity of satisfaction among rural residents with different attributes. Furthermore, an integrated approach of SEM and the multinomial logit (MNL) model is proposed to identify the key factors that affect rural residents’ bus travel-mode choice. The results indicate that reliability and convenience are the key factors affecting satisfaction with urban–rural bus service quality. There are significant differences in rural residents’ satisfaction by gender, age, income, education level, occupation, and travel-mode attributes. Urban–rural bus service quality has a significant positive impact on rural residents’ bus travel-choice behavior, and its degree of influence is greater than all explicit variables except private car ownership. The findings of this study can help operators and governments formulate policies to improve the service quality of urban–rural buses and ultimately promote the use of buses.

Optimal intersection design and signal setting in a transportation network with mixed HVs and CAVs

It is widely recognized that human-driven vehicles (HVs) and connected and autonomous vehicles (CAVs) are expected to coexist and share the urban traffic infrastructure in the transportation network for a long time. To fully utilizes CAVs’ potential to reduce congestion in the transitional period, this study proposes and addresses the intersection design and signal setting problem in the transportation network with mixed HVs and CAVs. Due to the difference in terms of communication technology and autonomous driving technology for HVs and CAVs, three types of intersections have been proposed to amplify the efficiency-improvement benefit from CAVs by separating CAVs from HVs in a temporal or local-spatial dimension: the conventional signalized intersection, the novel signalized intersection with a dedicated CAV phase and dedicated CAV approaches, and the intelligent signal-free intersection. The problem is to determine the spatial layout of different types of intersections in the transportation network, the cycle time, and green time duration for each phase of signalized intersections that minimize the total travel cost, in which the route choice behavior of heterogeneous travelers has been respected based on the user equilibrium principle. A mixed-integer nonlinear programming model is developed to formulate the proposed intersection design and signal setting problem based on the link-node modeling method, in which the path enumeration is avoided. Then, by employing various linearization techniques (e.g., disjunctive constraints, logarithmic transformation, piecewise linearization with logarithmic-sized binary variables and constraints, outer-approximation technique), the proposed model can be further transformed into a relaxed sub-problem in the form of mixed-integer linear programming. A globally optimal solution algorithm embedding with solving a sequence of relaxed sub-problems and nonlinear mixed complementarity problems is proposed to converge to a global optimum. The results of numerical experiments illustrate that the proposed methodology can significantly improve the performance of the whole network. Moreover, it consistently outperforms the optimization model considering only conventional signalized intersections under various CAV market penetration rates.

Effect of Cognitive Experiences and Attitudes on Commuters’ Travel Choice Behavior for Autonomous Vehicles

Effect of Cognitive Experiences and Attitudes on Commuters’ Travel Choice Behavior for Autonomous Vehicles

Travel Mode Choice Behavior for High-Speed Railway Stations Based on Multi-Source Data

The service quality of connecting travel mode at high-speed railway (HSR) stations plays an essential role in the travel chain. Most travel choice behavior studies are based on survey data and focus on special scenarios, such as going to work or school. Few studies have addressed travel mode choices for connecting to HSR stations. Based on multi-source data, this study aims to investigate the influences of travel time, cost, reliability, detour, and land use on connecting mode choice behaviors at HSR stations. Taking the Xi’anbei railway station as an example, this study analyzes the behavior characteristics of connecting modes including conventional buses, subway transit, and taxis, and builds a connecting travel mode choice behavior model for arrival/departure at HSR stations based on a generalized additive model (GAM). The results show that connecting travelers at HSR stations prefer to choose subways and taxis during the morning and evening peak hours. The perceived costs for arriving travelers are less sensitive than those for departing travelers. Arriving travelers are significantly more sensitive to reliability than departing travelers. Connecting travelers are less sensitive to cost and more sensitive to reliability than commuting travelers. The research results can provide suggestions and guidelines for connecting facility planning in HSR stations.

Modeling the Traveler’s Route Choice Behavior under Unexpected Accidents

To investigate the route choice behavior of travelers under unexpected accidents, this study designs four different scenarios of travelers’ route choice behavior experiments according to the severity of unexpected accidents. The bounded rationality characteristics of travelers, such as the time perception difference coefficient, psychological threshold effect coefficient, and scale effect, are introduced into the generalized random regret minimization (GRRM) model. An improved generalized random regret minimization (IGRRM) model is constructed based on travelers’ route choice behavior under unexpected accidents. The collected data of travelers’ route choice results under four different congestion-level scenarios are analyzed by the IGRRM model. The study finds that with the increase in congestion level, travelers are more willing to change the route; during the commute, travelers tend to choose the route with less travel time and angular cost; in the moderate and serious congestion scenarios, travelers no longer reject the detour route; attribute perception difference, scale effect, and psychological threshold effect; affect travelers’ route choice behavior. The IGRRM model based on the route choice behavior of travelers can more accurately characterize the route choice behavior of travelers under unexpected accidents, provide a basis for traffic flow distribution under unexpected accidents, and benefit traffic management departments to take traffic control and guidance measures to ease the traffic congestion caused by unexpected accidents.

Random Regret Minimization Model of Carpool Travel Choice for Urban Residents Considering Perceived Heterogeneity and Psychological Distance.

Carpooling is a sustainable, economical, and environmentally friendly solution to reduce air pollution and ease traffic congestion in urban areas. However, existing regret theories lack consideration of the heterogeneity of attribute perception in different ways and the psychological factors that affect regret, so they cannot accurately portray urban residents' carpool travel decisions and cannot provide a correct explanation of the actual carpool choice behavior. In this paper, based on the analysis of classical random regret minimization models and random regret minimization models considering heterogeneity, the concept of psychological distance is introduced to address shortcomings of the existing models and construct an improved random regret minimization model considering heterogeneity and psychological distance. The results show that the fit and explanatory effect of the improved model proposed in this paper is better than that of the other two models. The psychological distance of travel residents during the Corona Virus Disease 2019 (COVID-19) affects the anticipated regret value and the willingness to carpool. The model can better describe the carpool travel choice mechanism of travelers and effectively explain the carpool travel choice behavior of travelers.

Passenger Travel Path Selection Based on the Characteristic Value of Transport Services

In this paper, we establish a generalized cost function for passenger travel based on the characteristic value of transportation services, and we select high-speed rail, air, and air–rail as the selection branches in order to build a passenger travel decision-making model combined with a logit model to analyze the preference for passenger travel choices. The results show that, within the transportation network of the Chengdu–Chongqing economic circle, passengers are more likely to take the high-speed rail option directly, followed by air–rail and air options, and these results are concentrated within a transportation distance range of less than 1000 km, 1000–1200 km, and more than 1200 km, respectively. Among them, the OD travel routes comprised Chengdu and Yibin as the transit nodes of the combined travel account for more than 50%, which exhibits the high strategic development potential of air–rail combined transportation. Ridge regression analyses show that ticket price, quickness, convenience, and comfort influence the probability related to travelers’ travel choice at varying degrees. The elasticity values of the fatigue recovery time, travel time, and time value per capita for high-speed rail are much greater than the other two travel modes, indicating that these three factors have a high impact on the travel choice behavior of high-speed rail.

Influence of in-vehicle crowding on passenger travel time value: Insights from bus transit in Shanghai, China

In-vehicle crowding is an external cost imposed on passengers by public transport. It is necessary to quantify the crowding discomfort to measure its influence on passengers’ travel choice. This study presents a stated preference survey embedded with revealed preference to investigate the influence of in-vehicle crowding on the travel choice behavior. The in-vehicle crowding degree is measured as standing density. Considering the in-vehicle environment and the subjective perception of passengers, the classification criteria for the in-vehicle crowding degree of bus transit are put forward. Then, the mixed logit model is used to establish the utility function of bus passenger travel choice behavior under different in-vehicle crowding degrees, and the value of travel time is calculated. Also, the functional relationship between in-vehicle crowding and travel time value is identified. In addition, by converting in-vehicle crowding into time, the travel impedance model considering in-vehicle crowding and the crowding value of time multipliers are defined. A study of Shanghai bus transit shows that when the standing density is higher than 7.5 passengers per square meter, bus passengers are willing to exchange 12.83 Chinese Yuan RMB for 1-hour travel time saving, and the perceived in-vehicle time is twice as long as the actual in-vehicle time. This study provides a reference for improving the attraction of bus transit and easing the decline in bus rides.

Travel choice behaviour under uncertainty in real-market settings: A source-dependent utility approach

In this study, we present evidence on the source-preference difference between two natural sources of uncertainty with mode-specific travel time variability (public transport vs private car), while accounting for the role of experiential information in shaping the revealed ambiguity seeking in the context of travel mode choice. The proposed structural model jointly accounts for time-money trade-offs, diminishing sensitivity towards travel outcomes, perceptual conditioning, beliefs and ambiguity attitudes. We find that in the loss domain with possible travel time outcomes, decision makers may prefer more volatile travel time variability embedded in public transport systems, and with more experiential information, this preference would become stronger.

Travel satisfaction and travel well-being: Which is more related to travel choice behaviour in the post COVID-19 pandemic? Evidence from public transport travellers in Xi'an, China.

The spread of Coronavirus Disease 2019 (COVID-19) has decreased the willingness to choose public transport where travellers are more likely to be infected due to intensive passenger flow, in which case it is hard to attract passenger volume if the subjective well-being of travellers is not improved. However, the traditional measurement of travel evaluation may be not conductive to the context under the influence COVID-19 pandemic and it is necessary to analyse the changes of internal mechanisms of travel well-being to avoid the loss of passengers. Based on structural equation modelling, this paper explored the internal relationship between the constructs of travel well-being and emphasised the significance of taking psychological factors into consideration in the post COVID-19 pandemic. The results show that travel satisfaction towards epidemic prevention measures during travel is strongly related to overall travel well-being, which can be used as a key part of well-being measurement scale design in the future. The results also indicate that, due to negative mood on affective level induced by COVID-19, travel satisfaction on cognitive level is not directly but indirectly related to travel choice behaviour through overall travel well-being. Compared to travel satisfaction, travel well-being is more extensive and covers travel satisfaction to some extent. Therefore, instead of studying travel satisfaction simply, taking travel well-being incorporating travel satisfaction, mood and psychological distance as the dependent variable to identify shortages existing in public transport will provide a more accurate perspective for policy makers in the post COVID-19 pandemic.

Diagnostic Evaluation of Public Transportation Mode Choice in Addis Ababa

The major modes of public transportation in the city of Addis Ababa are buses and taxis. There is no rail transit within the city. The existing public transportation is of a low quality because of the limited number of buses and taxis, poor management, and bad behavior of drivers. Despite these prevailing problems, efforts to make an empirical study of the city’s urban transportation are insignificant. This research attempts to develop an ordered logit model to examine citizens’ perceptions of the bus condition, as a determining factor for their choice of bus transportation, and to develop a binary logit model to analyze traveler choice behavior. A diagnostic analysis is undertaken based on the two models. The result shows that citizens’ perceptions of the three chosen bus condition aspects (fare, convenience, and frequency) have a significant influence on public transport mode choice.

Measuring the resilience of an urban rail transit network: A multi-dimensional evaluation model

Once an urban rail transit (URT) system breaks down or is deliberately damaged, it will cause tremendous pressure on the whole urban transportation system. Hence, its ability to deal with incidents has become an important research field. Existing studies seldom consider the entire cycle of an incident and ignore changes in passenger travel behavior during emergency events. In this paper, resilience is defined as the ability of absorbing, resisting and recovering when the URT network fails. We propose a multi-dimensional evaluation model for measuring the resilience by calculating multiple abilities with consideration of multi-source data including passenger flow, train diagram, passenger travel choice behavior, network topology, etc. Finally, we present a case study based on real-world data from the Beijing Subway network to illustrate the effectiveness and applicability of the proposed model. Through the resilience analysis, we recognize the critical stations, measure emergency recovery ability and provide necessary information support for reducing the risk of URT incidents.

A game theoretic approach to study the impact of transportation policies on the competition between transit and private car in the urban context

This paper proposes a spatial gaming model to characterize the mode choice behavior of travelers on a corridor connecting a suburban neighborhood to the central business district. Previous game theoretic studies typically incorporate a discrete choice model into the gaming structure to observe travelers’ mode choice behavior. The novelty of the proposed model revolves around incorporating parking operator into the gaming structure. We also present a model extension accounting for the heterogeneity in travelers’ income. The parking operator and transit agency, as the two players of the game, seek to obtain a greater market share by controlling their prices. The nature of the transit agency as a (pseudo) public authority is modeled by letting the player maximize a weighted sum of profit and consumer surplus. The private parking operator, on the other hand, is a profit maximizer. With the out-of-pocket cost, generalized travel time, and disutilities other than time in the utility function of travelers, this gaming model enable us to evaluate the impact on auto-transit competition of a wide range of urban policies. A corridor in Isfahan, Iran is considered to demonstrate the applicability of the proposed model. Eight travel demand management policies are examined. We see that restrictive policies (e.g., cordon pricing) typically reduce the total demand while encouraging policies (e.g., improving transit access time by integrating transit with a bike-sharing system) increase it. The most effective policies are found to be hybrid policies such as increasing auto travel time coupled with lowering transit in-vehicle travel time.

Accessibility-based vulnerability analysis of multi-modal transportation networks with weibit choice models

This study proposes utility-based accessibility measures and a weibit-based analysis method for analyzing multi-modal transportation network vulnerability. To account for the multi-dimensional travel choice behavior in multi-modal transportation networks, an advanced weibit-based combined modal split and traffic assignment model is adopted to derive the accessibility measures based on the weibit-expected travel disutility from both route choice and mode choice dimensions. The accessibility at the modal split level is derived from the nested weibit model, while that at the route choice level is obtained based on the path-size weibit model. The proposed weibit-based vulnerability measures are inherently suitable for assessing the relative degradation in network performance and can measure the vulnerability of networks with heterogeneous scales without overestimating the importance of modes or routes sharing similar features. Using a simplified multi-modal transportation network of Hong Kong, numerical experiments are conducted to demonstrate the properties of the proposed multi-modal vulnerability analysis. The results indicate that the proposed analysis can effectively address similarity and heterogeneity issues in the analysis of choice behavior and network vulnerability, which are often ignored by the traditional logit-based measures.