Does the information provider choose to promote public transport use or protect public health? A case study in China

Urbanization leads to motorised transport in both, developed and developing countries. Particularly, in developing countries motorised transport has become primarily cause of negative health impacts such as air pollution, noise pollution, traffic accidents etc. Several studies illustrate that private vehicle use causes negative health impacts while public transport use has less negative health impacts and active transport use even supports human health. Therefore, influencing mode choice behaviour plays a pivotal role to change health impacts of transport. To pursue this objective, this study is carried out in two phases. In the first phase, a state-of-the-art mode choice model is developed, which analyses the change of mode choice behaviour. In the second phase, consideration of health impacts in transport demand management aims at influencing mode choice behaviour of travellers. There are several studies on developing mode choice models which are based on an analysis of mode choice behaviour. However, there is a lack of research which considers health impact awareness as an influencing factor in the mode choice model. Filling this gap, this study reviews the state of development in transport demand modelling, then proposing a model development process. Applying this process, a transport mode choice model with consideration of health impact awareness is developed. Experience from developed countries such as European countries, the United States of America, Australia, Singapore and other countries illustrates that transport demand management is effective in changing traveller’s behaviour. The change of mode choice behaviour has helped to reduce negative health impacts of transport in developed countries, already. Following the experience of developed countries, the state of development in transport demand management with consideration of health impacts is reviewed. From that, the lists of transport demand management measures are collected. Finally, the German method to develop transport demand management strategies is explained. The newly developed mode choice model is applied for the case study of Hanoi, Vietnam. For this city, the traffic situation, the transport planning strategies and the health impact problems are explained. The results of the model application confirm that mode choice behaviour of travellers is affected by seven explanatory variables and five latent variables. It is demonstrated that health impact awareness influences mode choice behaviour significantly. Corresponding to the identified health problems, transport demand management measures are selected. These measures were combined to strategies for the case of Hanoi, following the German method to develop transport demand management strategies. Based on results from the mode choice model, this study finally simulates the change of mode choice behaviour in some scenarios of transport demand management measures and strategies, thereby, demonstrating the effectiveness of this approach. In summary, this study enriches research on health impacts of transport in developing countries. The results show that novel factor health impact awareness is a significant influence on mode choice behaviour. The results also reveal that transport demand management is feasible to be applied for the case study of developing countries. The implementation of transport demand management is effective to reduce negative health impacts from transport and to support human health.

In recent years, traffic congestion has become increasingly serious and the urban environment has deteriorated, posing a challenge to the modern sustainable transportation system. Sustainable travel behavior is a solution that many scholars recognize as being an important aspect in the development of socially, environmentally, and economically sustainable communities. Increasing numbers of studies analyzed the travel choice behavior based on Random Regret-Minimization (RRM) model. RRM considers multiple attribute compromises to capture the traveler’s choice behavior based on minimizing the perceived regret decision criteria. Travel route choice and travel mode choice are interrelated and mutual restraint when a traveler makes a travel decision. To our knowledge, there are limited literatures that overall considered travel mode and travel route choice behavior based on RRM at present. This paper aims to fill this gap and presents a literature review for the application of RRM on sustainable travel mode and travel route choice behavior from empirical issues, influencing factors, theories and methods to evaluate RRM’s potential and limitations as a discrete model of travel choice behavior. The results will provide reference for researchers to study this field and develop novel strategies to promote the sustainable traffic system in the future.

While there is a general agreement that the use of public transportation should be encouraged, it has also been reported that the willingness and tendency to use the public transportation is dominated by the propensity of private car use. Consequently, the persistent increase in the modal share of private car has been a matter of concern in the context of growing cities. This research starts with the understanding that the entire state of the modal share in a city would be the collective reflection of the mode choice behaviour of individuals who populate the large population mass in the city. A causal feedback loop model is developed to study the individual mode choice behaviour in the context of a city. The model is then translated into a system dynamics simulation model. The context of Dubai has been considered in order to operationalise the simulation model. The simulation experiments revealed that it would be challenging to motivate individuals in using the public transportation in the city where private car has already been a dominant mode. It is also found that the very desire of individuals to own and use private car is one of the key points that should be addressed properly if the mode choice is to be influenced.

Abating the air emission related to the transportation sector by operation of the Bus Rapid Transport (BRT) system has been adopted in Indonesia. This study was conducted to obtain an overview of the implementation of BRT, the success of shifting private vehicles to BRT, and the number of emissions resulting from the operation of BRT. The study was conducted using a questionnaire and observations in BRT vehicles. A questionnaire survey was conducted randomly across Semarang sub-districts for 701 private vehicles consisting of cars and motorcycles in a parking lot. Questionnaires were distributed to BRT users in the waiting room and among those who left the BRT. The emission quantity is obtained from GPS observations installed in the BRT and quantified by the emission generation equation based on the bus speed. Even though they are not BRT users for daily activities, motorcycle users use BRT more frequently than private car users. For the private car and motorcycle users, the BRT coverage area is the first barrier to using the BRT system, followed by travel time (due to congestion and traffic jams). Based on current BRT users, the shifting of motorcycle users is far higher than private car users. About 30% of public transport users (besides BRT) shift to BRT users. The BRT emissions (CO and TSP) in the east-west corridor on weekdays and weekends are higher than those in the south-north corridor. Based on this study's results, the BRT application has not significantly reduced the use of private vehicles. Instead, shifting occurs from former public transport to BRT. BRT emissions are related to traffic route conditions and topography. BRT implementation needs to comprehensively consider social, economic and technical (infrastructure) aspects.

While there is a general agreement that the use of public transportation should be encouraged, it has also been reported that the willingness and tendency to use the public transportation is dominated by the propensity of private car use. Consequently, the persistent increase in the modal share of private car has been a matter of concern in the context of growing cities. This research starts with the understanding that the entire state of the modal share in a city would be the collective reflection of the mode choice behaviour of individuals who populate the large population mass in the city. A causal feedback loop model is developed to study the individual mode choice behaviour in the context of a city. The model is then translated into a system dynamics simulation model. The context of Dubai has been considered in order to operationalise the simulation model. The simulation experiments revealed that it would be challenging to motivate individuals in using the public transportation in the city where private car has already been a dominant mode. It is also found that the very desire of individuals to own and use private car is one of the key points that should be addressed properly if the mode choice is to be influenced.

Exploring tertiary students' travel mode choices in Auckland: Insights and policy implications

Estimating factors influencing mode choice behaviors has posed a significant challenge for decision makers. In this paper, we propose a three-step method to estimate mode choice between public transportation and private vehicles within a heterogeneous metropolitan area. First, we implement a deep learning model, Altaïr, capable of inferring travel times and travel flows by leveraging multi-source input data (Step 1). To identify homogeneous sub-regions regarding mode choice behaviors, two data clustering models are performed: k-means and a Gaussian Mixture Model (GMM, Step 2). The GMM reveals three spatial clusters based on the relationships between relative travel times and relative travel flows in public transportation and private vehicles. Moreover, an econometric model (robust ordinary least squares) is employed to identify additional explanatory variables, including sociodemographic features and location variables (Step 3). This hybrid method is currently employed in the Paris metropolitan area (France). At the metropolitan level, we find that competitive travel times in public transportation lead to higher ridership. Conversely, when the time ratio exceeds approximately 3.5–4, public transportation use becomes negligible in comparison to private vehicles. The method's results align with those of the regional Household Travel Surveys (HTS) regarding the individual attributes influencing public transportation use (share of women and students in the municipality of residence) or private vehicle use (share of car owners and people over 55). There is significant car use in peripheral areas, but public transportation accessibility promotes higher flows in this mode everywhere, with the exception of central Paris. • We use a three-step method to estimate mode choice behaviors in a metropolitan area. • A deep learning model infers travel times/ travel flows from multi-source input data. • Using the relationship between the two variables, a GMM reveals spatial clusters. • An econometric model highlights other explanatory variables for mode choice behaviors. • Our method can guide transport policies in areas where HTS are too difficult or costly to implement.

In developed countries such as Canada and United States, a significant number of individuals depend on automobile as their main mode of transport. There has been a stronger push towards analyzing travel behavior at the individual level so that transportation agencies can formulate appropriate strategies to reduce the auto dependency. Towards this pursuit of enhancing our understanding on travel behavior, we examine individual home to work/school commute patterns in Montreal, Canada with an emphasis on the transit mode of travel. The overarching theme of this paper is to examine the effect of the performance of the public transportation system on commuter travel mode and transit route choice (for transit riders) in Montreal. We investigate two specific aspects of commute mode choice: (1) the factors that dissuade individuals from commuting by public transit and (2) the attributes that influence transit route choice decisions (for those individuals who commute by public transit). This study employs a unique survey conducted by researchers as part of the McGill University Sustainability project. The survey collected information on commute patterns of students, faculty and staff from McGill University. In addition, detailed socio-demographic and residential location information was also collected. The analysis was undertaken using multinomial logit model for the travel mode choice component and a mixed multinomial logit model for the transit route choice component. The model estimation results were employed to conduct policy sensitivity analysis that allows us to provide recommendations to public transportation and metropolitan agencies.

The nonlinear effect of atmospheric conditions on middle-school students’ travel mode choices

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.

This paper firstly analyzes the comparative advantages of carpooling under the mobile Internet and traditional travel modes, including buses, private cars and taxis, as well as the differences between carpooling under the mobile Internet and traditional carpooling, so as to obtain the factors that affect travelers’ mode choices. Secondly, the mixed logit model is used to describe the travelers’ travel mode choice behavior, which effectively avoids the limitations of the IIA characteristics and preference randomness of the logit model. Finally, we conducted an SP survey on 1077 samples online and offline. After eliminating some invalid samples, 984 valid ones were obtained. Based on these survey data, we analyze the impacts of carpooling under the mobile Internet on the mode shares of traditional travel modes. The results show that for different trip lengths, carpooling under the mobile Internet has different degrees of substitution for buses, taxis and private cars. That is to say, travelers who previously chose buses and other modes will shift to carpooling due to the mobile Internet. In addition, in most cases, the emergence of carpooling under the mobile Internet is helpful to reduce the traffic volume in the urban road network, thus alleviating the urban congestion. However, when the trip length is short and the seat utilization ratio of carpooling under the mobile Internet is low, carpooling under the mobile Internet will increase the traffic volume.

Changes in travel modes used by people, particularly reduction of the private car use, is an important determinant of effectiveness of transportation plans. Because of dependencies between the choices of residential location and travel mode, integrated modelling of these choices has been proposed by some researchers. In this paper, an agent-based microsimulation model has been developed to evaluate impacts of different transport development plans on choices of residential location and commuting mode of tenant households in Tehran, the capital of Iran. In the proposed model, households are considered as agents who select their desired residential location using a constrained NSGA-II algorithm and in a competition with other households. In addition, they choose their commuting mode by applying a multicriteria decision-making method. Afterwards, effects of development of a new highway, subway and bus rapid transit (BRT) line on their residential location and commuting mode choices are evaluated. Results show that despite the residential self-selection effects, these plans result in considerable changes in the commuting mode of different socio-economic categories of households. Development of the new subway line shows promising results by reducing the private car use among the all socio-economic categories of households. But the new highway development unsatisfactorily results in increase in the private car use. In addition, development of the new BRT line does not show significant effects on the commuting mode change, particularly on decrease in the private car use.

Based on the analysis of travel survey data, this paper developed and applied the mixed binary-multinomial choice modeling method to the exploration of the relationship between travel mode choice and trip chaining behavior of home based commute. The analysis is carried out from two directions, namely trip chaining to travel mode, and travel mode to trip chaining. The developed model takes into consideration the effects of interdependencies between trip chaining and travel mode choice on each choosing behavior, which emphasizes the guidance of decision mechanism on travel behavior forecasting. The results show the pattern that trip chaining drives mode choice is the dominating trend. This means that travelers first decide how to organize different activities and travel into trip chains based on individual and family needs and then make travel mode choice condition under the limit of trip chaining. These findings have important implications for travel demand forecasting and for the design and carrying out of demand management measures.

This study examines the environmental impacts of roundtrip car sharing services by investigating transportation behavior. Car sharing should contribute to reduced greenhouse gas GHG emissions; however, such schemes include both positive and negative environmental effects, including: (1) reduced CO2e (carbon dioxide equivalent) from substituting private vehicle use for more fuel-efficient car sharing vehicles, (2) increased CO2e as car-less individuals switch from public transit to car sharing vehicles and (3) reduced CO2e due to fewer vehicles. This study examines the impacts of this modal shift on greenhouse gas (GHG) emissions using three types of models: a mixed logit model to analyze car sharing service preferences; a binary logit model to analyze whether individuals are willing to forgo vehicle ownership or planned purchases to use car sharing services; and a linear regression to determine how much private vehicle or public transportation use would be replaced by car sharing and the resulting effects on mobility. Total emissions from the current car sharing market equal 1,025,589.36 t CO2e/year. However, an increase in electric vehicle (EV) charging stations to 50% of the number of gasoline-fuel stations would increase the probability of electric car sharing vehicle use, thereby reducing emissions by 655,773 t CO2e. This study shows that forgoing vehicle purchases does not offset the increased GHG emissions caused by the shift from public transportation or private vehicle use to car sharing.

To quantify travel demand, it is necessary to understand the travelers' mode choice behavior. The Logit model is widely used in travel mode choice because of its closed form. Nevertheless, the variance of the utility function is unchanged in Logit-based models, indicating that the perceived error of the traveler on the option is fixed as the utility changes, which is inconsistent with the actual situation. While in Weibit-based models, travelers' perception error of options grows with the increase of the utility. Moreover, the relative difference is captured, and the asymmetric property exists, which is different from Logit-based models. This paper contributes to the literature by comparing the performances of the Logit-based and Weibit-based models. In this article, six discrete choice models for travel mode choice are discussed based on data of Swiss metro, which includes multinomial Logit model, multinomial Weibit model, and derived models. The Weibit-based models outperform the Logit-based models, considering with the adjusted likelihood ratio index of all models in this paper.