Modelling travellers’ route choice behaviours with the concept of equivalent impedance

Transportation - A novel concept is presented to capture route choice behaviours and to account for the correlation of routes in the logit model. The issue that route choice models are easily...     

Understanding the effects of travel demand management on metro commuters’ behavioural loyalty: a hybrid choice modelling approach

Transportation - As part of efforts to promote sustainable mobility, many cities are currently experiencing the rapid expansion of their metro network. The consequent growth in ridership motivates...     

Will autonomous vehicles change auto commuters’ value of travel time?

This paper examines the potential impact of autonomous vehicles on commuters’ value of travel time (VOTT). In particular, we focus on the effect on auto commuters in small and medium-sized metropolitan areas, concerning the spatial variability across urban areas, suburbs, and rural areas. We design a stated choice experiment to elicit potential changes in 1,881 auto commuters’ valuation of travel time in autonomous vehicles and apply a mixed logit model to quantify the changes in the value of travel time if taking autonomous vehicles. The results of this study suggest that the effect of autonomous vehicles on the VOTT is spatially differentiated. We find that riding in a private autonomous vehicle reduces the commuting VOTT of suburban, urban, and rural drivers by 32%, 24%, and 18%, respectively, compared to 14%, 13%, and 8% for riding in a shared autonomous vehicle. Finally, we discuss the implications of these lower values of time on transportation and land use planning.     

The effects of a compressed working week on commuters’ daily activity patterns

This study was based on 220 randomly selected government employees of the University of the Philippines in Los Baños, and was carried out after the Philippine government implemented an experimental two-month compressed working week scheme. The study aimed to examine compressed working week-related lifestyle pattern changes, especially changes in activity–time patterns. The moderating effects of compressed working week to the commuters were also explored. Survey results showed that two-hour increases in the compressed working week workday substantially impacted activity–travel patterns. Commuters reduced household activities by about one hour, sleeping time by about 20 minutes, and pre-work preparation time by about 30 minutes. Commuting times also significantly declined during the compressed working week. This study also identified factors that moderated the compressed working week plan’s effects on activity–travel patterns.     

Analysis of the effect of charging needs on battery electric vehicle drivers’ route choice behaviour: A case study in the Netherlands

Electric travelling appears to dominate the transport sector in the near future due to the needed transition from internal combustion vehicles (ICV) towards Electric Vehicles (EV) to tackle urban pollution. Given this trend, investigation of the EV drivers’ travel behaviour is of great importance to stakeholders including planners and policymakers, for example in order to locate charging stations. This research explores the Battery Electric Vehicle (BEV) drivers route choice and charging preferences through a Stated Preference (SP) survey. Collecting data from 505 EV drivers in the Netherlands, we report the results of estimating a Mixed Logit (ML) model for those choices. Respondents were requested to choose a route among six alternatives: freeways, arterial ways, and local streets with and without fast charging. Our findings suggest that the classic route attributes (travel time and travel cost), vehicle-related variables (state-of-charge at the origin and destination) and charging characteristics (availability of a slow charging point at the destination, fast charging duration, waiting time in the queue of a fast-charging station) can influence the BEV drivers route choice and charging behaviour significantly. When the state-of-charge (SOC) at the origin is high and a slow charger at the destination is available, routes without fast charging are likely to be preferred. Moreover, local streets (associated with slow speeds and less energy consumption) could be preferred if the SOC at the destination is expected to be low while arterial ways might be selected when a driver must recharge his/her car during the trip via fast charging.     

Investigation on lowering commuters’ in-cabin exposure to ultrafine particles

To determine ways to reduce commuters’ ultrafine particle exposure, factors such as ventilation condition, mechanical airflow rate, driving speed, cabin air filter quality, and cabin air filter use are examined. The results show that the in-cabin to on-roadway ratio is reduced by 20% when the fan is set to recirculation-on versus when set to recirculation-off because fewer ultrafine particles are exchanged between the inside and outside of the cabin. Also, when the fan is set to recirculation-off, the ratio is reduced by 40% at lower mechanical airflow rates. The thickest cabin air filter resulted in a 30% in-cabin to on-roadway ratio decrease compared with the thinnest. Thus, driving conditions with the least UFP in-cabin to on-roadway ratio is when a vehicle is operating with a high efficiency cabin filter, the ventilation set to fan-on and recycling is recirculation on, and at a high ventilation airflow rate. Furthermore, recirculating in-cabin air through a high efficiency particulate air filter was found to significantly reduce in-cabin UFP exposure.     

Metro commuters’ satisfaction in multi-type access and egress transferring groups

This paper considers both the access and egress stages as an entire process to analyze the satisfaction levels of commuters with metro commuter journeys. Based on a survey in Nanjing, China, seven intermodal travel groups are employed as targets for this analysis. The groups include Walk–Metro–Walk, Walk–Metro–Bus, Bike–Metro–Walk, Bike–Metro–Bus, Bus–Metro–Walk, Bus–Metro–Bus and Car–Metro–Walk, which are named according to the modes of transportation that are employed for access and egress trips. Binary logit models are developed for each group to identify the main factors of satisfaction level. The results show that access and egress stages serve important but different roles in the seven groups. Facility service qualities in two stages are the primary factors that affect overall satisfaction. The groups with same access or egress modes have significantly different core factors. Access by bike and bike–metro–transit users are concerned with bike parking safety, whereas bike–metro–walk users value parking spaces near metro stations. With two transfers between bus and metro, transit–metro–transit users indicate that the weak point in the access stage is the crowded spaces on buses. However, transit–metro–walk users value bus on-time performance, which is also valued by groups with metro–bus egress transfers. For egress by walking, commuters that use motorized modes for access are concerned with the egress walking environment, whereas users of non-motorized access modes are more concerned with egress walking spaces. The findings of this study are helpful for policy developments than can improve public satisfaction with commutes by urban metro.     

Multi-criteria route planning based on a driver’s preferences in multi-criteria route selection

In this study, some different approaches were designed, implemented, and evaluated to perform multi-criteria route planning by considering a driver’s preferences in multi-criteria route selection. At first, by using a designed neuro-fuzzy toolbox, the driver’s preferences in multi-criteria route selection such as the preferred criteria in route selection, the number of route-rating classes, and the routes with the same rate were received. Next, to learn the driver’s preferences in multi-criteria route selection and to classify any route based on these preferences, a methodology was proposed using a locally linear neuro-fuzzy model (LLNFM) trained with an incremental tree based learning algorithm. In this regard, the proposed LLNFM-based methodology reached better results for running-times, as well as root mean square error (RMSE) estimations in learning and testing processes of training/checking data-set in comparison with those of the proposed adaptive neuro-fuzzy inference system (ANFIS) based methodology. Finally, the trained LLNFM-based methodology was utilized to plan and predict a driver’s preferred routes by classifying Pareto-optimal routes obtained by running the modified invasive weed optimization (IWO) algorithm between an origin and a destination of a real urban transportation network based on the driver’s preferences in multi-criteria route selection.     

Train commuters’ scheduling preferences: Evidence from a large-scale peak avoidance experiment

We study the trip scheduling preferences of train commuters in a real-life setting. The underlying data have been collected during large-scale peak avoidance experiment conducted in the Netherlands, in which participants could earn monetary rewards for traveling outside peak hours. The experiment included ca. 1000 participants and lasted for multiple months. Holders of an annual train pass were invited to join the experiment, and a customized smartphone app was used to measure the travel behavior of the participants. We find that compared to the pre-measurement, the relative share of peak trips decreased by 22% during the reward period, and by 10% during the post-measurement. By combining multiple complementary data sources, we are able to specify and estimate (MNL and panel latent class) departure time choice models. These yield plausible estimates for the monetary values that participants attach to reducing travel time, schedule delays, the number of transfers, crowdedness, and unreliability.     

Assessing the influence of indicators’ complexity on hybrid discrete choice model estimates

Transportation - Hybrid discrete choice (HDC) modeling requires indicators to allow for the identification of latent variables. An indicator usually expresses the level of agreement of a respondent...     

A choice model of airline passengers’ spending behaviour in the airport terminal

This study examines the influence of passengers’ travel-related factors, their intention to shop and the impact of socio-economic factors on their consumption behaviours while at the airport terminal. Three categories of consumption model – shopping, dining and airport entertainment – are developed to analyse different consumption behaviours. The results show that free time is the main factor that influences passengers to choose entertainment while waiting at the terminal, and the use of airline lounges has a negative influence on the extent of engagement in all three types of passenger consumption behaviour, especially dining. Furthermore, passengers’ dining expenditure has a positive effect on the extent of engagement in entertainment, but a negative influence on the extent of engagement in airport shopping. Passengers’ preferences of airport shopping area after they have cleared security is positively associated with the extent of engagement in both shopping and entertainment but negatively associated with the extent of engagement in dining activities.     

An integrated analysis of workers’ physically active activity and active travel choice behavior

Using Texas add-on sample data from the 2009 National Household Travel Survey, this study examines adult workers’ daily active choice decisions in the context of physical activity and attendant health benefits. The study looked at workers’ two choice behaviors: active activity and active travel. The first choice behavior, active activity, is developed as an ordered-response model based on the number of physically active recreational activities pursued during the workday. The second choice behavior, active travel, is developed as a binary-response model that examines workers’ active travel choices—whether or not the worker used any active mode of travel during the same workday. The study improves the understanding and knowledge of observed factors influencing workers’ physically active activity-travel behavior. The study also provides several observations regarding the role (and constraints) of employment in individuals’ active choices. Using a flexible copula modeling methodology, we explore the true correlation (or dependence) between the two behavior choices that could occur due to the presence of unobserved factors, suggesting a simultaneously low or simultaneously high propensity for being physically active across workers. The study findings suggest that transportation and public health policy makers can mutually benefit from encouraging workers to be physically active (from an activity and/or travel perspective). Overall, the study draws attention to the integrated nature of the public health and transportation fields, thereby providing a distinct view of active/inactive choice behavior. To our knowledge, this is the first study exploring a rich variety of components for workers’ active activity-travel behavior through a robust copula approach.     

Where do cyclists ride? A route choice model developed with revealed preference GPS data

To better understand bicyclists’ preferences for facility types, GPS units were used to observe the behavior of 164 cyclists in Portland, Oregon, USA for several days each. Trip purpose and several other trip-level variables recorded by the cyclists, and the resulting trips were coded to a highly detailed bicycle network. The authors used the 1449 non-exercise, utilitarian trips to estimate a bicycle route choice model. The model used a choice set generation algorithm based on multiple permutations of path attributes and was formulated to account for overlapping route alternatives. The findings suggest that cyclists are sensitive to the effects of distance, turn frequency, slope, intersection control (e.g. presence or absence of traffic signals), and traffic volumes. In addition, cyclists appear to place relatively high value on off-street bike paths, enhanced neighborhood bikeways with traffic calming features (aka “bicycle boulevards”), and bridge facilities. Bike lanes more or less exactly offset the negative effects of adjacent traffic, but were no more or less attractive than a basic low traffic volume street. Finally, route preferences differ between commute and other utilitarian trips; cyclists were more sensitive to distance and less sensitive to other infrastructure characteristics for commute trips.     

Do millennials value travel time differently because of productive multitasking? A revealed-preference study of Northern California commuters

Transportation - Millennials, the demographic cohort born in the last two decades of the twentieth century, are reported to adopt information and communication technologies (ICTs) in their everyday...     

Quantifying drivers’ acceptance of renewable fuel standard: Results from a choice experiment in South Korea

The production and use of renewable fuels in the transport sector are rapidly increasing. Renewable fuel standard (RFS) is a strong regulatory component and quantitative policy expected to have a significant market impact. In Korea, RFS implementation was agreed upon in July 2013 and will be enforced beginning in July 2015. Drivers’ acceptance is the most important consideration for RFS introduction and sustainable implementation. This study analyzed Korean customer preferences for RFS and quantified their acceptance level according to policy design. A choice experiment was analyzed with a mixed logit model to reflect the heterogeneity of respondents’ preferences. Respondents were relatively sensitive to the price increase, while other attributes had little effect on acceptance of RFS. Differences between the influences of attributes on drivers’ acceptance should be considered when designing RFS implementation. Furthermore, it is recommended that the price of transportation fuels should be limited to an increase between KRW 10 and 20/liter (USD 8.879 × 10⁻³ and 1.776 × 10⁻²/liter) to ensure high acceptance level, secure a budget for infrastructure, and achieve substantial environmental improvement.     

Investigating the impacts of weather variability on individual’s daily activity–travel patterns: A comparison between commuters and non-commuters in Sweden

Understanding travel behaviour change under various weather conditions can help analysts and policy makers incorporate the uniqueness of local weather and climate within their policy design, especially given the fact that future climate and weather will become more unpredictable and adverse. Using datasets from the Swedish National Travel Survey and the Swedish Meteorological and Hydrological Institute that spans a period of thirteen years, this study explores the impacts of weather variability on individual activity–travel patterns. In doing so, this study uses an alternative representation of weather from that of directly applying observed weather parameters. Furthermore, this study employs a holistic model structure. The model structure is able to analyse the simultaneous effects of weather on a wide range of interrelated travel behavioural aspects, which has not been investigated in previous weather studies. Structural equation models (SEM) are applied for this purpose. The models for commuters and non-commuters are constructed separately. The analysis results show that the effects of weather can be even more extreme when considering indirect effects from other travel behaviour indicators involved in the decision-making processes. Commuters are shown to be much less sensitive to weather changes than non-commuters. Variation of monthly average temperature is shown to play a more important role in influencing individual travel behaviour than variation of daily temperature relative to its monthly mean, whilst in the short term, individual activity–travel choices are shown to be more sensitive to the daily variation of the relative humidity and wind speed relative to the month mean. Poor visibility and heavy rain are shown to strongly discourage the intention to travel, leading to a reduction in non-work activity duration, travel time and the number of trips on the given day. These findings depict a more comprehensive picture of weather impact compared to previous studies and highlight the importance of considering interdependencies of activity travel indicators when evaluating weather impacts.     

Network structure,capacity growth and route hierarchies: the case of China’s air traffic system revisited

This paper examines air traffic patterns among China’s scheduled airlines in January 2006 and January 2011, using Official Airline Guide data on carrier schedules. The author classifies Chinese carriers into one of four classes. Airports are also organized into a classification scheme based on several criteria related to the total volume of traffic, the carriers serving the airports and the nature of the airports to which they are connected. Counts, sums, percentage shares and changes in these calculations between 2006 and 2011 are presented in tabular form. Inferences about the fundamental structure and future patterns of capacity growth for the yet not fully emerged Chinese air traffic system can be drawn.     

Modelling travellers’ risky choice in a revealed preference context: a comparison of EUT and non-EUT approaches

Recent work on risky choice modelling has sought to address the shortcomings of expected utility theory (EUT) by using non-expected utility theoretic (non-EUT) approaches. However, to date these approaches have been merely tested on stated choice data which is flexible and cheap. In this study, we empirically investigate the feasibility and validity of non-EUT approaches in a revealed preference (RP) context in which travel time distribution is extracted from observed historical travel time data, and subsequently present systematic comparisons between EUT, weighted utility theory, rank-dependent expected utility theory, and prospect theory (PT). The empirical evidence indicates that each non-EUT model has important behavioural insights to offer, moreover, EUT as well as non-EUT models can be applied to the RP context. However, the EUT and non-EUT model fits are generally similar with only PT providing a marginally improved model fit over EUT. The key findings presented in this study reinforce the importance of exploring non-EUT models within a revealed preference context before they can be applied reliably to modelling risky choices in the real world.     

I can’t believe your attitude: a joint estimation of best worst attitudes and electric vehicle choice

The number of conventionally fuelled motor vehicles in use is increasing worldwide despite warnings about finite fossil fuel and the detrimental impacts of burning such fuels. While electric vehicles, the subject of much research, generate far less emissions and offer the potential for power from renewable sources, they are yet to significantly penetrate the market. Tangible barriers such as price and vehicle range still exist, but consumer attitudes also drive behaviour. This paper examines attributes in a framework relatively new to transportation and energy policy; best–worst scaling. This method is widely considered an improvement over traditional methods of eliciting attitudes and beliefs, where respondents select attitudes they find best or worst from a set of attitudinal statements. To avoid potential endogeneity bias, we jointly model attitudes and choice for the first time with best–worst data. It is found that energy crisis, air quality and climate change concerns influence behaviour with respect to vehicle range and that travel behaviour change and forms of government incentives are needed influences on behaviour with respect to vehicle emissions. It is argued that correctly modelling attitudes reduces the error term of the vehicle choice model and provides policy makers with an improved lens for assessing behaviour. Additionally, the methods described within can easily be adapted to other policy scenarios.     

Empirical test of a constrained choice discrete model: Mode choice in São Paulo,Brazil

This paper examines the properties and empirically tests a model of discrete choice which incorporates probabilistic choice set generation. Denominated the Parametrized Logit Captivity (PLC) model, it is a generalization of the well-known “dogit” specification. The PLC model is shown to be theoretically and empirically more flexible than the latter. Work mode choice data collected in a 1977 O/D survey in São Paulo, Brazil, is used to obtain parameter estimates, as well as to evaluate consumer reaction to a series of perturbations in travel time, travel cost and income, for both the PLC and Multinomial Logit models. Comparisons between the two specifications are made in terms of statistical fit, reasonableness of predictions and differences in predictions across models.