Mode Choice Model Research Articles

Urban Transportation Mode Choice Model in Bali

Urban Transportation Mode Choice Model in Bali

Global air freight flow data for aviation policy modelling

Models of air freight are often constrained by a lack of available data. This study brings together different sources of air freight supply and demand data to address this gap. To study air freight operations, we combine schedules, flight tracking data and country-level databases of passenger and freight movements to produce estimates of global flight segment-level capacity and load factors in freighter aircraft and passenger holds for 2019–2021. To study true origin-ultimate destination air freight demand, a freight mode choice model by commodity group is developed for 2019 to fill gaps in mode information in international and national trade datasets, and estimates are made for 2019 and 2021. Initial comparisons of supply and demand data demonstrate that air freight journeys differ significantly from passenger journeys, typically including more flight legs (roughly, around 2, compared to 1.2 for passengers) and greater leg distances (2.2–2.5 times average passenger distance), with significant asymmetry in commodity flows and operations to and from individual countries and regions. These differences persist in 2021, despite COVID-19 induced shifts towards carrying more air freight in freighter aircraft. This research forms a first step towards making available an integrated database of estimated global air freight flows by commodity.

A dynamic discrete choice modelling approach for forward-looking travel mode choices

In this paper, we present a systematic approach based on dynamic discrete choice models (DDCM) to investigate individuals’ forward-looking mode choice behaviours in daily travel tours with multiple destinations. We propose a novel network transformation model that encompasses the entire decision space of all feasible mode combinations for every observed trip/tour in the dataset. By applying the well-established Recursive Logit model structure commonly used in path choice modelling, we address the tour mode choice problem effectively and quantify forward looking considerations in the mode choice process. The proposed model captures the complex considerations individuals take into account when making mode choices. The network transformation incorporates downstream mode limitations into the preceding mode choice options, enabling us to model individuals’ forward-looking behaviour and gain insights into how considerations of future trips such as a shopping in the evening, or school pick-up trip influence previous mode choice decisions earlier in the day. Uncovering and quantifying these hidden forward-looking factors can help modellers better explain the private car usage usually observed for the entire sequences of daily trips, even in presence of competitive alternative modes. The proposed network transformation also enables us to measure the effect of the requirement/preference to return private vehicles (car, motorcycle, and bicycle) home on mode choices in home-bound trips, and subsequently, on the entire daily mode choice decisions. To validate the proposed model, we utilise the VISTA household travel survey data from the Melbourne Metropolitan area in Australia. The model is compared against baseline models, demonstrating its statistical advantages. Additionally, intuitive illustrations using the data showcase the model’s efficacy. From transport planning and policy perspective, tour-based mode choice modelling provides a more comprehensive and precise understanding of travel behaviour by considering the sequence of trips made by an individual. This can help capture the interactions and dependencies between different trips, which trip-based models may overlook. The proposed model is more suitable for analysing the effects of policy interventions like congestion pricing, public transport investments, or new mobility initiatives, as they can better represent the cascading effects of such policies across multiple trips.

A method for evaluating the effectiveness of improving public transport services, parking fee policies, and park-and-ride facilities in order to encourage the use of public transport

Enhancing the quality of public transport services, implementing effective parking fee policies, and developing park-and-ride facilities are common solutions that urban planners and city governments apply to encourage public transport use. However, estimating the impact of these measures on increasing public transport usage during the solution design process is challenging. This article proposes an approach to assess the effects of these measures in the solution design process, which policymakers or city governments can apply to formulate suitable policies and feasible measures aligned with the local context. The approach begins with a survey on travel behaviour, followed by formulating a mode choice model to assess the sensitivity of the mode share to factors affecting mode choice. This approach is applied to Da Lat city, and the research results indicate how to enhance public transport services, determine appropriate parking fee levels, and establish park-and-ride facilities to achieve a public transport share of 5%, 10%, 15%, or higher. One of the main research results shows that the Park and Ride solution is feasible. Additionally, to achieve a growth rate of public transport volume up to 10%, it is necessary to improve public transport quality services combined with an increase in car parking fees in the downtown area

Cycling or Ropeway – Two Choices of Environmentally Friendly Urban Modes

Ropeways have proven to be a suitable extension to public transport in Latin American cities. Numerous ropeway projects are being discussed in Europe. These have not yet progressed beyond the planning stage because, among other reasons, no assessments of potential demand have been carried out. Current travel demand models do not consider mode-specific properties of ropeways and cycling competing with public transport. Based on a stated choice experiment, a mode choice model for people without car availability was estimated. Besides standard attributes like travel times and costs, the impact of mode-specific attributes like service rates, crowding, reliability, personal attitudes and weather conditions were tested. A Mixed-Logit model was estimated showing significant influence of these attributes on mode choice in Graz, an Austrian city with a mode share of 20% each for cyclists and public transport. The introduction of a ropeway can increase the share of public transport, as the high capacity and reliability of ropeways have a positive effect. However, it is unlikely that some groups of cyclists will use the ropeway, as travel time and cost are factors that are more important. The database and the modeling approaches can also be applied to other ropeway projects.

Assessing the spatial transferability of mode choice models: A case of shared electric mobility hubs (eHUBS) in Amsterdam and Manchester

Electric mobility hubs (eHUBS) represent an innovative approach to providing diverse shared electric transportation options, aimed at curbing private car use, and mitigating associated environmental impacts. Assessing the impact of eHUBS on travel choices across different cities requires significant resource and time investment due to the need for localized data collection and model development. This paper proposes a potential solution to this challenge by investigating the transferability of mode choice models originally developed for eHUBS in Amsterdam to predict behaviour towards eHUBS in Manchester.Multinomial Logit (MNL) and mixed logit models were transferred using four different procedures, and their effectiveness was evaluated using three assessment measures. The findings indicate that a scaled mixed logit model with an updated Alternative Specific Constant (ASC) outperforms other models in terms of its transfer effectiveness, both for disaggregate and aggregate assessment measures. The interplay between transfer procedures and assessment measures also was examined, with results indicating enhancements in disaggregate transferability measures with the 'scaling' transfer procedure, while 'updating the Alternative Specific Constants (ASCs)' improved predictions of aggregate mode shares. Following the analysis, the paper presents an in-depth discussion to provide a nuanced understanding of transferability and thus offers valuable insights for researchers planning future studies and practical considerations for policymakers.

FORECASTING FUTURE PUBLIC TRANSPORT MODE CHOICE BEHAVIOUR OF COMMUTERS IN BAHRAIN USING LOGIT AND CLASSIFICATION TREE MODELS: A COMPARATIVE STUDY

Global trade and social relationships are greatly facilitated by transportation. However, the majority of nations, including Bahrain, face substantial challenges with their transportation systems. For the development of technical solutions that can promote the progress of these transport systems, it is now crucial to have a complete understanding of travel demands and driver's characteristics. This paper aims to explore the influential factors concerning travel mode choice in Bahrain and utilize mode choice models to forecast the probable utilities of various future public transport modes. The study utilizes diverse, 3864 data records extracted from previous surveys as well as a recent one conducted within this research. Subsequently, using Minitab software, two types of mode choice models were built, namely the logit model and the classification tree model, focusing on modelling the future transportation system, considering potential public transport modes (Public Bus, Metro, and Tram). The analysis of the data identified trip cost as the top predictor, moreover, direct, and quick travel, accessibility, and convenience were also found to significantly influence the choice of travel mode in Bahrain. Additionally, the findings indicate that the metro is the preferred choice for future public transport, with a strong preference observed for a combination of metro and tram. The research also suggests, in terms of model performance, that when capturing more complex patterns, as in this study, the classification tree outperforms the multinomial Logit model. Overall, the research provides valuable insights into mode choice in Bahrain and highlights the important factors influencing commuting decisions. The results of this study can support the development of an efficient public transportation system that would satisfy the needs and preferences of commuters in Bahrain and ultimately lead to a sustainable and accessible transportation infrastructure in the country.

How Does the Introduction of Shared Ride-Sourcing Services Affect Demand for Existing Modes for Non-Commuting Trips? Evidence from a Joint RP-SP Study in Metro Vancouver

The introduction and subsequent growth of ride-sourcing services have been found to affect the use of existing modes of travel. Although prior studies have explored the impacts of these services as a whole, relatively little work has been done to explore the relationship between shared ride-sourcing and existing modes of travel. Given the potential for shared ride-sourcing to help mitigate the negative externalities associated with ride-sourcing, understanding the factors influencing the use of these services and their relationship with existing modes can inform efforts to help ensure that this potential is realized. This study uses data from a web-based survey of Metro Vancouver residents to estimate a joint revealed preference–stated preference (RP–SP) model of mode choices for non-commuting trips. The model is then applied to explore the potential impacts of shared ride-sourcing on the demand for existing modes. To the authors’ knowledge, this is the first study to use a joint RP–SP model to explore the potential impacts of shared ride-sourcing on the demand for existing modes. The results suggest these services can affect the demand for exclusive ride-sourcing and attract demand from more sustainable modes such as public transit and active modes. This information can be used to help inform policies that help ensure that the benefits of shared ride-sourcing are realized. Shared ride-sourcing use can be encouraged by increasing the difference between the cost of exclusive and shared services; however, limiting the impacts of these services on the demand for more sustainable modes is also important.

Designing an Autonomous Mobility-on-Demand Service for Transit Last-Mile Access

Public transportation has the potential to provide a safe, convenient, affordable, and eco-friendly mobility service. However, because of its fixed routes and limited network coverage, it is sometimes difficult or impossible for passengers to walk from a transit stop to their destination. This inaccessibility problem is also known as the “transit last-mile connectivity problem.” Such a lack of connectivity can force travelers to drive, consequently increasing vehicle ownership and vehicle miles traveled (VMT) on the roads. The autonomous mobility-on-demand (AMoD) service, with characteristics such as quick fleet repositioning and demand responsiveness, has the potential to provide last-mile coverage where fixed-route transit can only provide limited coverage. This study aims to address the last-mile problem by developing an online demand-responsive AMoD service integrated with fixed-route transit. A linear time-delay dynamical system is proposed to model the AMoD system, and a model predictive control methodology is adopted to regulate the system around an equilibrium point with minimum vehicle rebalancing. To assess the impact of this new mobility service on travel demand, a simulation study is developed and integrated with a mode choice model capturing a combined transit-AMoD model. The experiment results reveal the potential to enhance transit efficiency, reduce VMT, and accommodate the increased transit demand while maintaining the quality of service.

Joint Routing and Pricing Control in Bimodal Mixed Autonomy Networks with Elastic Demand and Three-Dimensional Passenger Macroscopic Fundamental Diagram

The interaction between mixed autonomy traffic and public transport vehicles competing for limited road space is a less explored area of research. To evaluate the traffic dynamics in bimodal networks, a three-dimensional (passenger) network fundamental diagram, known as 3D-NFD (3D-pNFD), can be estimated that relates the accumulation of cars (autonomous and human-driven) and buses to the network vehicular (passenger) travel production. In this study, we propose a 3D-pNFD-based congestion pricing scheme considering three vehicle classes of buses, system-optimal (SO) seeking connected and automated vehicles (CAVs), and user-equilibrium (UE) seeking human-driven vehicles (HVs). We develop an iterative tri-level modeling framework for mode choice, pricing, and route choice in a mixed autonomy network. The lower level generates mixed equilibrium traffic flow through an integrated mixed equilibrium simulation-based dynamic traffic assignment model and a transit assignment model. The mid-level finds the optimal toll rates through a 3D-pNFD feedback-based controller. A nested logit-based mode choice model is also applied to capture travelers’ preferences toward three available modes and incorporates elastic demand. To encourage CAVs to follow the SO routing mode, they are provided with a discount on the congestion toll whereas UE-seeking HVs are subject to full price toll. Buses are also entirely exempt from the toll. We explore three scenarios with different discount rates on SO-seeking CAVs to investigate the effect of the incentive plans on the mode choice behaviors of road users in the pricing zone. The performance of the proposed model is evaluated in a large-scale network in Melbourne, Australia.

Interpretable Machine Learning for Mode Choice Modeling on Tracking-Based Revealed Preference Data

Mode choice modeling is imperative for predicting and understanding travel behavior. For this purpose, machine learning (ML) models have increasingly been applied to stated preference and traditional self-recorded revealed preference data with promising results, particularly for extreme gradient boosting (XGBoost) and random forest (RF) models. Because of the rise in the use of tracking-based smartphone applications for recording travel behavior, we address the important and unprecedented task of testing these ML models for mode choice modeling on such data. Furthermore, as ML approaches are still criticized for leading to results that are hard to understand, we consider it essential to provide an in-depth interpretability analysis of the best-performing model. Our results show that the XGBoost and RF models far outperform a conventional multinomial logit model, both overall and for each mode. The interpretability analysis using the Shapley additive explanations approach reveals that the XGBoost model can be explained well at the overall and mode level. In addition, we demonstrate how to analyze individual predictions. Lastly, a sensitivity analysis gives insight into the relative importance of different data sources, sample size, and user involvement. We conclude that the XGBoost model performs best, while also being explainable. Insights generated by such models can be used, for instance, to predict mode choice decisions for arbitrary origin–destination pairs to see which impacts infrastructural changes would have on the mode share.

Relax on the way to work or work on the way to relax? Influences of vehicle interior on travel time perceptions in autonomous vehicles

The impending arrival of autonomous vehicle (AV) technology has the potential to transform how individuals perceive time spent travelling. By removing the need to drive and pay attention to the road, AV users could perform other activities, including those for work or leisure. As a result, AVs are expected to lower the burden of travel and, therefore, the value of travel time (VOTT). Despite the significant impacts that AVs may have on individuals’ choices and the transportation system, few have studied their impacts on travel time perceptions, and even fewer have examined the extent to which these impacts will vary depending on the types of tasks that can be performed within an AV. This study uses stated preference data collected in Fall 2022 to develop mode choice models and subsequently quantify how the availability of three types of AV: privately-owned, exclusive, and pooled AV may shift perceived travel times in the Greater Toronto and Hamilton Area. The error-component mixed logit models highlight the cross-nesting between privately-owned AVs and driving. In addition, this study is the first in Canada to distinguish the VOTT reductions by AV type, trip purpose, and interior description (which caters to different tasks). VOTT reductions as large as 42% less than driving a conventional vehicle were estimated. The results of this study provide additional empirical evidence for AV VOTT reductions (particularly in the Canadian context) and can be used to help craft policies in preparation for the arrival of AVs.

Cycling speed variation: a multilevel model of characteristics of cyclists, trips and route tracking points

AbstractSmooth cycling can improve the competitiveness of bicycles. Understanding cycling speed variation during a trip reveals the infrastructure or situations which promote or prevent smooth cycling. However, research on this topic is still limited. This study analyses speed variation based on data collected in the Netherlands, using GPS-based devices, continuously recording geographical positions and thus the variation in speeds during trips. Linking GPS data to spatial data sources adds features that vary during the trip. Multilevel mixed-effects models were estimated to test the influence of factors at cyclist, trip and tracking point levels. Results show that individuals who prefer a high speed have a higher average personal speed. Longer trips and trips made by conventional electric bicycles and sport bicycles have a higher average trip speed. Tracking point level variables explain intra-trip cycling speed variations. Light-medium precipitation and tailwind increase cycling speed, while both uphill and downhill cycling is relatively slow. Cycling in natural and industrial areas is relatively fast. Intersections, turns and their adjacent roads decrease cycling speed. The higher the speed, the stronger the influence of infrastructure on speed. Separate bicycle infrastructure, such as bike tracks, streets and lanes, increase speed. These findings are useful in the areas of cycling safety, mode choice models and bicycle accessibility analysis. Furthermore, these findings provide additional evidence for smooth cycling infrastructure construction.

Applying a Bayesian network for modelling the shift from motorcycle to public transport use in Vietnam

Transport mode choice models commonly incorporate factors such as utility maximisation, socio-ecological considerations, socio-demographic characteristics, and psychological factors. However, there is still a lack of integrated models linking objective factors, such as travel mode attributes, demographic & socio-economic characteristics and built environmental characteristics, with subjective factors, such as attitudes, personal norms, and perceived behavioural control, to better understand the commuters’ modal choices. Furthermore, a clear research gap exists on mode choice behaviour in Southeast Asian countries, where motorcycling is widespread. This study aims to address these research gaps by analysing data gathered from 618 Vietnamese motorcyclists between December 2020 and February 2021. Reliability analysis and a Bayesian network are used to identify the objective and subjective factors influencing the intention to use public transport among Vietnamese motorcyclists. The findings highlight that among five subjective factors, personal norm is the most sensitive parameter in calculating the posterior probability distribution of intention and decision to use public transport along with other objective factors, namely travel time and travel cost. The results also reveal that motorcyclists aged 23 + have a higher perceived value of travel time than those aged 23–. In contrast, motorcyclists aged 23– and female motorcyclists have a higher perceived value toward environment-friendly travel modes, such as public transport. At the same time, they do not try to differentiate themselves from the societal standards in terms of motorcycle usage. The findings of this study provide valuable insights to local urban/transport planners and policymakers in Southeast Asian countries.

Exact and heuristic approaches for the Modal Shift Incentive Problem

It is widely acknowledged that freight transportation in general, and road freight transportation in particular, contributes significantly to greenhouse gas and pollutant emissions. In response, many countries have implemented incentive schemes to divert freight traffic from roads towards more environmentally friendly transportation modes (e.g., rail, maritime). However, incentive-based policies to promote modal shift are often sporadic and uncoordinated across non-road modes, thus preventing full leverage of dedicated budgets. In this study, we present a strategic problem encountered by national and supranational entities aiming to reduce emissions from the freight transportation sector, referred to as the Modal Shift Incentive Problem (MSIP). The problem involves designing incentive policies in the form of subsidies aimed at reducing the cost of eco-friendly transportation modes, with the aim of minimizing the volume of freight transported via less environmentally friendly modes. These incentive policies must satisfy various constraints, including a limited budget for incentives, equity requirements, and compliance with regulations pertaining to competitiveness and free-market dynamics. To address the MSIP, we propose an original mathematical formulation on the basis of a deterministic threshold modal choice model used to simulate the shipper behavior in selecting the transportation modes. Then, we develop a two-phase solution method, able to determine good solutions with a small computational burden based on the generation of a subset of promising incentive schemes. Finally, we solve real-world instances generated utilizing data derived from a comprehensive case study in Italy. The results on different instance types, in terms of number of shipments, budget, and costs, show the relationship between these factors, the complexity of the MSIP, and the modal shift.

Teaching freight mode choice models new tricks using interpretable machine learning methods

Teaching freight mode choice models new tricks using interpretable machine learning methods

Sensitivity evaluation of machine learning-based calibrated transportation mode choice models: A case study of Alexandria City, Egypt

Intelligent methods including Machine Learning (ML) techniques have been increasingly employed in transportation mode choice modeling, which is more complex than other demand models, since it has to reliably and accurately reflect a wide range of related categorical and continuous variables, concerning the travelers, transportation system, and trip characteristics. ML techniques can capture such complex relationships. So that, they can provide a more nuanced understanding of the travelers’ decision process. Most research studies focused mainly on the evaluation of the model accuracy, where little has been done to evaluate the models’ performance toward transport attributes. This research aims to calibrate the transportation choice models using ML techniques, then conduct a comparison with the Multinomial Logit (MNL) model to identify the impact on the model accuracy and performance and quantify to what extent the ML models are sensitive to transport policies compared to the traditional MNL model. To this end, eight ML classifiers were examined. As a case study, the models were calibrated to reflect the choice behavior of trip makers in Alexandria City, Egypt. The models were successfully calibrated with satisfying accuracy; however, the ML models have better calibration results in terms of predictability, outperforming the MNL model, where the GBDT classifier records the best prediction accuracy. Finally, the sensitivity analysis test was performed to quantify the elasticity of the models to transport policies. The results show the ML models’ structure is more comprehensively and accurately built than the MNL model providing better indicative and reliable sensitivity analysis results.

Missing Typical Weekdays in Travel Surveys: A Pseudo-Panel Approach to Explore Weekly Travel Patterns

Travel surveys generally rely on single-day travel diaries where respondents report their travel information for a typical weekday. However, the concept of a typical weekday does not represent the current reality, as travel behavior has been largely altered in the post-pandemic period. Besides, the conclusions based on analyzing single-day travel diaries lack the ability to capture daily variations in travel behavior. In response to these concerns, this research proposed a framework to expand single-day travel diaries into longitudinal multi-day travel data using a pseudo-panel approach. Leveraging the constructed longitudinal data, the study evaluated the determinants of people’s daily participation in work–school, routine, and discretionary activities. Fixed and random effects panel data estimation models were used for this purpose. Results showed that activity participation is largely attributed to vehicle ownership, income, education, driving license, and household structure. Noticeable daily trends were observed in work–school and discretionary activities. A negative association between transit pass ownership and activity participation was noticed, suggesting social exclusion faced by transit users. In addition, teleworkers were found to be relatively more engaged in discretionary activities. Suburban residents were found to travel longer to participate in activities compared to urban dwellers. The proposed research framework can support future activity-based modeling aspects, such as activity participation, scheduling, mode choice, shared travel, and destination choice models, specifically addressing the “typical weekday” barrier.

Statistical modelling of travel mode choice of public university students in Freetown, Sierra Leone: the case of three campuses

ABSTRACT Though much research has been conducted on students’ travel behavior around the world, not much is known about university students in Sierra Leone. This study investigates the most common transport modes amongst public university students, the factors influencing their preference, and the impact of their choice on the environment. Through a Revealed Preference survey, using the traditional paper-and-pencil method combined with a smartphone-assisted interviewing technique, 632 students were sampled. Discrete travel choice models were developed and estimated, and the methodology was demonstrated for the three campuses under the University of Sierra Leone. The study found that more than one-third of students preferred the paratransit mode, ‘podapoda’, followed by taxi, two and three-wheelers. According to the mode choice model results and exploratory analysis, sociodemographics, travel time, cost, and trip distance are the main factors considered by students’ for selecting a transport mode. Students varied widely in their assessment of how their mode choice affects the environment. These findings highlight the need for a sustainable transport system for university trips. Acquiring appropriate transport modes, promoting active travel, and developing student residences with shorter commutes will ensure long-term land management. This will promote the sociocultural, economic, educational, and ecological aspects of campus growth.

Assessing airport ground access interventions: An integrated approach combining mode choice modeling and microscopic traffic simulation

Airport ground accessibility is an important and integral part of airport planning, which significantly impacts airport attractiveness and its environmental sustainability. In this paper, we propose the combined use of road micro-simulation with meso-scope discrete choice modeling to support the evaluation of airport access interventions. The proposed integrated framework allows us to appraise mode choice and infrastructural interventions simultaneously to accurately assess the performance of airport access road networks in accommodating larger traffic. We validate and demonstrate the benefits of the proposed approach by applying it to a real-world case study based on the Milan–Bergamo Airport (BGY), in which (i) we conduct an ex-post counterfactual analysis to assess the impact of the interventions undertaken to mitigate the large growth (54.6%) in the years 2013–2019, and (ii) evaluate future scenarios up to 2030 considering the additional expected growth and introduction of direct rail services. Results indicate that BGY’s ground access interventions have contributed to effectively accommodating traffic growth by significantly mitigating road congestion and associated pollutant emissions. Results also inform on the need and effectiveness of additional interventions to meet the substantial expected future growth.