Preferences for stop-based pooled automated vehicles in mode choice models across trip purposes in Flanders, Belgium

A pooled RP/SP mode, route and destination choice modeling approach to capture the heterogeneity of mode and user type effects in Austria

The transport sector has been hit hard by the COVID-19 pandemic disrupting travel behaviors and mobility patterns around the globe. The pandemic has also affected mode choice behavior. This research study modeled the mode choice behavior before and during the COVID-19 pandemic in Pakistan. Data was collected through an online questionnaire survey consisting of questions about socio-economic characteristics, factors affecting mode choice, and mode chosen for shorter as well as longer distances for both before and during COVID-19 pandemic situations. The results indicated that public transport use declined, whereas walking and bicycling slightly increased during the pandemic. The respondents placed more priority on safety and security, comfort, cleanliness, infection concerns, personal social status, availability of hand-sanitizers, waiting, and paying more for less congested vehicles during the pandemic. Factor analysis was performed to explore the underlying factors affecting mode choice before and during the pandemic. Discrete choice models were developed to model the mode choice behavior. Monthly household income and pandemic-related underlying factor were significant predictors of mode choice for shorter distances (i.e., < 5 km) during the pandemic. Whereas, gender, car ownership and monthly household income were significant predictors of mode choice for longer distances (i.e., > 5 km) during the pandemic. Understanding the modal shift during a pandemic will surely help urban and transport planners to prepare better for effective transport management in the future. Policy implications are also presented to help policymakers in developing policies for post-pandemic mobility needs, particularly in developing countries.

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.

Joint destination-mode travel choice models are developed for intercity long-distance travel among sixteen cities in Yangtze River Delta Megaregion of China. The model is developed for all the trips in the sample and also by two different trip purposes, work-related business and personal business trips, to accommodate different time values and attraction factors. A nested logit modeling framework is applied to model trip destination and mode choices in two different levels, where the lower level is a mode choice model and the upper level is a destination choice model. The utility values from various travel modes in the lower level are summarized into a composite utility, which is then specified into the destination choice model as an intercity impedance factor. The model is then applied to predict the change in passenger number from Shanghai to Yangzhou between scenarios with and without high-speed rail service to demonstrate the applicability. It is helpful for understanding and modeling megaregional travel destination and mode choice behaviors in the context of developing country.

To research the systematized impacts of psychological factors on travel mode choice behavior, the paper constructed three multiple indicators and multiple causes (MIMIC) models for motorcycle, bus and car, according to the expanded theory of planned behavior (TPB) including a variety of psychological factors with respect to commuter mode choice behavior. The fitted values of latent variables from estimation of MIMIC as explanatory independent variables were induced into the conditional logit model (CLM), which is often called a hybrid choice model (HCM). The empirical data were collected and used to compare the difference influences of parameters between the traditional discrete choice model (DCM) and HCM. The results indicated that not all of the psychological latent variables have significant effects on mode choice behavior. The outcome of estimation of HCM has a higher degree of fit and much more robust predictions than the traditional DCM without latent variables.

This paper develops a combined mode choice and traffic assignment model that incorporates ridesharing as an option in a mode choice model, attempting to quantify the ridesharing market share in an equilibrium context. The mode choice model takes into account that the waiting time for a ride is dependent on the available drivers. The traffic assignment model is a static user equilibrium that interacts with the discrete choice model through level of service variables. An iterative algorithm was implemented and applied in a simple network and a more realistic network. The results indicate that the quantity of ride sharing drivers is a key parameter to the service success, and below a critical mass of drivers, it is unlikely that passengers will find the service valuable. It is also shown that ride sharing has the ability to reduce in-vehicle times for all the users, although passenger may suffer from longer door-to-door times, having to wait for their ride.

Values of time have been defined in various forms such as value of leisure time (shadow price of time), value of travel time, and value of saving time, and are mostly measured based on individuals' travel choice behavior. The main purpose of this study is to estimate the value of leisure time by general mode choice models. The estimated level can be used to evaluate the benefits from the increasing leisure time gained by people in Taiwan after the government has practiced a series of policies to shorten employee's working hours in the last few years. To justify the application, this study reviews and reinterprets the theoretical results of some major works on value of time derivations. Then to practically estimate the value of leisure time, it suggests a method of combining revealed preference and stated preference data for application. Finally, it conducts an empirical study on travelers' mode choices behavior in Taiwan to carry out the method suggested. The value of leisure time is estimated at 56NT$ per hour (around 1.65US$/hr), which is even lower than the minimum wage rate regulated by Taiwan government.

Mode choice behaviour is often modelled by discrete choice models, in which the utility of each mode is characterized by mode-specific parameters reflecting how strongly the utility of that mode depends on attributes such as travel speed and cost, and a mode-specific constant value. For new modes, the mode-specific parameters and the constant in the utility function of discrete choice models are not known and are difficult to estimate on the basis of stated preferences data/choice experiments and cannot be estimated on the basis of revealed preference data. This paper demonstrates how revealed preference data can be used to estimate a discrete mode choice model without using mode-specific constants and mode-specific parameters. This establishes a method that can be used to analyze any new mode using revealed preference data and discrete choice models and is demonstrated using the OViN 2017 dataset with trips throughout the Netherlands using a multinomial and nested logit model. This results in a utility function without any alternative specific constants or parameters, with a rho-squared of 0.828 and an accuracy of 0.758. The parameters from this model are used to calculate the future modal split of shared autonomous vehicles and electric steps, leading to a potential modal split range of 24–30% and 37–44% when using a multinomial logit model, and 15–20% and 33–40% when using a nested logit model. An overestimation of the future modal split occurs due to the partial similarities between different transport modes when using a multinomial logit model. It can therefore be concluded that a nested logit model is better suited for estimating the potential modal split of a future mode than a multinomial logit model. To the authors’ knowledge, this is the first time that the future modal split of shared autonomous vehicles and electric steps has been calculated using revealed preference data from existing modes using an unlabelled mode modelling approach.

Dhaka, the capital of Bangladesh and one of the fastest growing megacities of the world, is already subjected to acute traffic congestion on a regular basis. Increasing the physical capacity to relieve congestion is however not feasible since already more than 70% of the area is built-up (Bari and Hasan, 2001). This has recently prompted the Government to prioritize the introduction of Mass Rapid Transit (MRT) options like Bus Rapid Transit (BRT) and Metro Rail in the city. Planning these MRT options however require rigorous mode choice models that can be used to predict ridership and quantify the willingness-to-pay (WTP) of the travellers. Though Dhaka is an old city (dating back to 16 th century), very few travel demand models have been developed for the city so far. Among the previous studies, four step travel demand models were developed in Dhaka Metropolitan Area Integrated Transport Study (DITS, 1993), Strategic Transport Plan (STP, 2005) and Dhaka Urban Transport Network Development Study (DHUTS, 2010) as well as by Habib (2002) and Hasan (2007) . However, in each case, the mode choice component was simplified and had grave limitations. In DITS, the mode choice model was simplified to a binomial choice model between private and public modes. In Habib (2002), an MNL model structure was adapted for the mode choice but the calibration results were counterintuitive with positive sign of the coefficients for time and cost parameters. In STP (2005), which is the most extensive travel demand model for Dhaka in recent years, a wide-scale household interview survey has been conducted for the first time. In the mode choice component of STP (2005), only two modes were considered i.e. Public Transport (PT) and Individualized Motorized Vehicles (IMV). In the IMV group, cars and taxis were grouped together overlooking their very different attributes (e.g. running cost, availability, accessibility, etc.) and the non-motorized vehicles (rickshaw) were not considered in spite of the fact that 37% of the person trips in Dhaka were made by rickshaw as reported in the same study (STP, 2005). Further, the STP model has adapted pre-set rules for determining choice-sets and ignored the heterogeneity among respondents. In Hasan (2007), a rule based choice model was adapted for car and a Multinomial Logit (MNL) model was adapted for the choice among rickshaw, auto-rickshaw, taxi and bus. Hasan’s model was based on STP data but the level-of-service (LOS) variables were updated using supplemental survey (for cost) and outputs of the software EMME/2 (for travel time). The potential measurement errors introduced in this process have however been ignored. In DHUTS (2010), a two step mode choice model has been developed where only two explanatory variables have been used: travel cost and Origin-Destination (O-D) shortest path distance (derived from network analysis). As evident from the description above, the existing mode choice models for Dhaka are based on pre set rules, ad-hoc choice-sets and network derived LOS values (without any correction for measurement errors). Further, the models are not robust enough to account for the new MRT, particularly since the LOS of MRT will vary significantly from the current modes. It may be noted that, the limitations of the available datasets played key roles behind the deficiencies of the previously developed mode choice models and this has prompted the current research where we present a comprehensive mode choice model which overcomes the limitations of the previous models and is robust enough to capture the preferences for the proposed MRT modes. In this paper, the STP data have been explored in detail, the key modeling issues have been identified and modeling approaches have been proposed to account for the data limitations. The improvements from the proposed approaches have been demonstrated by comparing the Value of Time (VOT) values. The rest of the paper is organized as follows: A short description of the Revealed Preference (RP) data highlighting the main limitations of the data and the description of the Stated Preference (SP) data collected as part of this research are presented first. This is followed by a description of the model framework. In the subsequent section, the estimation results of all the model components are presented which is followed by the VOT comparisons. The summary of findings and directions of future research are presented in the end.

A model system that predicts travel mode and party size jointly, is developed. This model system accounts for the correlation between party size and mode choice that arises due to omitted variables that affect the two. The coefficients of mode choice utility functions are assumed to vary with party size. The parameters are estimated using activity diary data and applying the bivariate probit model. The parameter estimates indicate that mode choice behavior is associated with party size, party size is affected by person attributes and the trip purpose, and the value of time increases with party size.

This study aimed to understand demand of park-and-ride (PNR) during different times of day. Surprisingly, there is no significant research in the area of dynamic demand of PNR. To fill this gap, this study of PNR demand was done in three stages. First, to understand why PNR users choose one PNR lot versus another, PNR lot choice models were developed. PNR lot choice behaviour was studied using two decision constructs, random utility maximization (RUM) and random regret minimization (RRM). Second, in order to understand the nature of utilization of PNR lots, a discrete time hazard model was developed based on the car arrival data in the morning period, at PNR lots. Finally, mode choice models (including PNR as one of the modes) were prepared to understand the choice of PNR as a mode.From the developed lot choice models, it is understood that the PNR users’ choice of PNR lot could also be explained by the RRM concept. In absence of any applications of RRM in PNR modelling, these new models serve as an important contribution. Further, the lot choice models suggested that the utilization of PNR lots is endogenous in nature. The identification of utilization as an endogenous variable is performed for the first time. The correction of endogeneity is completed using a two-stage control function method. Since the correction of endogeneity in the case of discrete choice transport models is a relatively new area, this work serves as additional evidence of the value of correcting for endogeneity using the control function method.This research modelled the utilisation of parking spaces using a discrete-time logistic regression model and calculated the probability that each parking space is occupied at the end of one of 60 time-intervals between 4:00am and 9:00am on a weekday. The findings from the model suggest that the probability of a parking space to be occupied increases with a larger capacity of the PNR lot, a larger number of public transport services, and a lower walking time to the platforms. Moreover, the results suggest that a parking space is more likely to be occupied in PNR lots farther from the CBD until 8.00am, but it is more probable to be occupied in PNR lots closer to the CBD from 8.00am onwards.Further, to understand the choice of PNR as a mode, mode choice models were prepared. With the aim of capturing a household’s long-term decisions (like owing car, motorbike, bicycle etc.) on everyday short-term decision like mode choice, a portfolio-based multinomial logit framework was used to model the mode choice behaviour; where portfolios are simply the set of modes enabled by the resources. Apart from conforming to some established results such as travellers are likely to choose modes which minimize their travel time), results suggested that long-term decisions do have an effect on the mode choice decisions. Further, a generalised nested logit (GNL) model was prepared as an alternative to the portfolio framework. In this model the portfolios (defined in the former model) act as nests. The GNL model was also able to capture the unobserved ‘perceived activity set’ of travellers as was the portfolio based model.To connect the lot choice and mode choice model, the composite utility form of the lot choice model was used as a variable in the mode choice model. However, the variable is not significant, indicating that the results do not necessarily suggest that travellers will change their mode when they do not find parking at the PNR lots.In overall, by answering questions such as why travellers choose one mode versus another and why PNR users choose one PNR lot versus another for different times of the day, and how PNR lot’s utilization changes for different times of the day, this research explored the PNR demand and established that PNR demand is dynamic in nature.

Increasing automobile use leads to higher costs for traveling associated with emissions, congestion, noise, and other impacts. One option to address this is to introduce high parking charges to reduce the demand for automobile use and encourage the travel mode switch to public transport. To estimate commuters’ mode choice behavior in response to high parking fees, commuters from Nanjing completed an individually customized discrete choice survey in which they chose between driving and taking the bus or metro when choices varied in terms of time and cost attributes. Multinomial logit models were used to estimate commuters’ responses to high parking charges. In the models, the variability of travel times is considered and analyzed in the stated mode choice models. The results suggest that increases in costs of driving will lead to a great reduction in driving demand. The travel time reliability ratio is 0.50 and the value of each minute late is almost 5.0 times more than the average travel time with the restriction of the maximum allowed delays. The methods used in this study could be adopted to estimate the effect of variable pricing strategies on mode choice responses for different trip purposes. The high value given to travel time variability has implications for transport policy in terms of decision making with respect to new pricing strategies. Moreover, the valuation of travel time savings taken into account in this study would be helpful to better understand the effect of high parking fees.

A recent project addressed how travelers would react to fuel prices rising above the high levels that were reached in mid 2008. Study participants were recruited during phone interviews, in the course of which trips made on a specified day were recorded. On the basis of one of those trips and the respondents’ possession of mobility tools, stated preference (SP) experiments were constructed. The first part consisted of a mode choice situation under modified price (and travel time) settings (tactical decisions). The second part focused on long-term (strategic) choices between the current and an alternative fleet, including a redistribution of yearly mileage. From the SP data, multinomial logit models for mode and fleet choice were estimated. The mode choice models were estimated by using income- and distance-dependent nonlinear utility functions and separately for the various trip purposes (as was the practice in earlier Swiss studies on similar topics) and controlled for all relevant trip characteristics. The models for mobility tool ownership, which were formulated by using a new approach, aimed to yield trade-offs between the various attributes of the offered fleets and to forecast the distribution of annual transit passes under modified settings. The findings suggest that inertia is present in both mode choice and mobility tool ownership. Elasticities do not change much from previous studies, where more-conservative price increases were assumed. Transit pass ownership is expected to grow only when increasing fuel prices coincide with stable public transport fares.

Anticipating long-distance travel shifts due to self-driving vehicles

The aim of this study is to analyze the elasticity of factors which influence the demand of public transport in London and Yogyakarta, based on the study of Paulley et al (2006) and the study of Sugiyanto (2007). The mode choice model between private cars and public transport (city bus) was developed based on users preferences as indicated by travel attributes. Five travel attributes were assumed to have high influences toward mode choice behavior, i.e; travel cost, congestion charge, travel time, headway of public transport (city bus), and walking time to the bus stop. The logit binomial model was used to formulate the individual behavior based on the stated preference data obtained from private car users in the Malioboro corridor. The mode choice model between private cars and city bus was developed based on 520 options from 65 respondents who used private cars to go to Malioboro to be as through traffic. Based on the direct and cross elasticity, the travel cost attribute has the highest elasticity value. Travel cost is the most sensitive attribute which influences the election probability of private cars and city bus. The factors which influence the demand of public transport in London are fares, quality of service, income, and car ownership. Keywords: mode choice, stated preference, and elasticity