Commuter Preferences Research Articles

Designing stable ride-sharing for commuting trip chain with role flexibility

AbstractIn ride-sharing, fixing the roles of commuters with vehicles could come at an opportunity of missed balance between riders and drivers. The decisions made by commuters with vehicles about roles are related to both to-work travel and return-home travel. Commuters may choose not to drive when they have a confirmed ride back home. This study designs a stable ride-sharing model with role flexibility for the commuting trip chain. The preferences of commuters over matching partners in trip chains are formulated. A stable matching algorithm based on commuting trip chain is designed. To assess the performance of the proposed model and algorithm, real-life data from a resident trip survey in Dalian are used to design experimental cases. The results indicate that stable matching based on the trip chain is not a consequence of the repetition of successive one-period stable matching. Compared with matching in two phases, the matching rate of commuters increases by an average of 10%. When the number of commuters with role flexibility increases by four times, the matching rate of commuters increases by an average of 5.6%.

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.

Insights into Passenger Transport Choices: A Mumbai Case Study

Understanding and studying passenger preferences in passenger transportation systems is important to optimise transportation infrastructure and increase passenger satisfaction. This paper presents a comprehensive case study conducted in Mumbai, India, which aims to understand the diverse preferences of commuters using different modes of transport within the city. Adopting a mixed-methods approach, data were accumulated by means of surveys, interviews, and observational studies conducted across different demographic sectors and geographical locations of Mumbai. The study analysed factors influencing commuters' mode choice, including cost, comfort, convenience, travel time, safety, and environmental concerns. The findings revealed significant differences in the preferences of commuters, impacted by factors like socioeconomic status, occupation, travel distance, and access to infrastructure. While public transportation emerged as the dominant mode for daily commuting, preferences were nuanced, some demographics prefer private modes like cars and motorcycles. Additionally, the study identifies areas of improvement in the existing transport infrastructure and suggests measures to enhance the reliability, frequency, safety, and connectivity of public transport services. The knowledge gleaned from this research provides valuable guidance for policymakers, urban planners, and transportation authorities in Mumbai, offering opportunities to tailor transportation solutions that align with commuters' preferences, thereby promoting sustainable and efficient passenger transport systems in the city.

Uncovering the Heartbeat of Public Transport in Klang Valley—Is Your Journey Satisfying?

This paper explores the essential elements of public transport service quality to enhance commuter preferences in Klang Valley, Malaysia. It specifically investigates reliability, assurance, tangible aspects, and responsiveness as key dimensions impacting customer satisfaction while riding on public transport among the urban residents in the Kuala Lumpur urban area. The study's main aim is to examine relationships among these dimensions and their overall influence on customer satisfaction using quantitative methods, with multiple linear regression analysis and convenience sampling used. The research seeks to capture a representative sample size to provide insights for targeted improvements in the overall public transport system.

Integrated Departure Time and Parking Location Choices in a Morning Commute Problem under a Partially Automated Environment

This study formulates the joint decisions of commuters on departure time and parking location choices in a morning commute problem where the commuters travel with autonomous vehicles (AVs) or human-driven vehicles (HVs). Under a mixed traffic environment, we aim to explore the impacts of parking capacity and parking pricing on the equilibrium travel pattern and the system performance. We build a dynamic equilibrium model for the morning commute problem by assuming that the parking slots can be grouped into central and peripheral clusters based on the distance between the parking location and the workplace. We first analyze the parking location preferences of commuters towards the two parking clusters under a mixed traffic environment. We then examine the equilibrium conditions and identify all the equilibrium travel patterns. We further analyze the system performance measured by the total travel cost with respect to the parking prices and the capacity of the central cluster. The optimal parking pricing scheme is also derived to minimize the total travel cost. We conduct numerical analysis to demonstrate the change in the total travel cost against the parking capacity of the central cluster and its parking price. Sensitivity analysis is performed to show the impacts of the network configuration on the total travel cost.

Identifying Commuter's Preferences of Feeder Modes for First and Last-Mile Connectivity: A Case Study of Kochi

Identifying Commuter's Preferences of Feeder Modes for First and Last-Mile Connectivity: A Case Study of Kochi

Commuter preferences for a first-mile/last-mile microtransit service in the United States

Commuter preferences for a first-mile/last-mile microtransit service in the United States

Modeling Commuter Preferences for the Proposed Bus Rapid Transit in Dar-es-Salaam

The paper analyzes individual commuter preferences towards the proposed bus rapid transit (BRT) system in Dar-es-Salaam, Tanzania. The objective of the survey was to identify how commuters perceive and value the proposed BRT service quality attributes. A stated preference survey of potential users of the proposed BRT was administered to 684 commuters who traveled to the central business district (CBD) on a regular basis. To this end, a special pictorial score card was developed that was suited for the local context and needed to capture the preferences of the commuter respondents. The BRT attributes considered for study are travel time, travel fare, and comfort. The stated choice data were analyzed using a binary logit model. The findings reveal, in order of importance, that comfort is the most valued attribute compared to travel time and travel fare, respectively.

Investigating Commuter Preferences for On-Demand Mobility Services Considering Working Flexibility: A Hybrid Choice Modeling Approach

Investigating Commuter Preferences for On-Demand Mobility Services Considering Working Flexibility: A Hybrid Choice Modeling Approach

Partnerships in Urban Mobility: Incentive Mechanisms for Improving Public Transit Adoption

Problem definition: Because of a prolonged decline in public transit ridership over the last decade, transit agencies across the United States are in financial crisis. To entice commuters to travel by public transit instead of driving personal vehicles, municipal governments must address the “last-mile” problem by providing convenient and affordable transportation between a commuter’s home and a transit station. This challenge raises an important question: Is there a cost-effective mechanism that can improve public transit adoption by solving the last-mile problem? Academic/practical relevance: In this paper, we present and analyze two incentive mechanisms for increasing commuter adoption of public transit. In a direct mechanism, the government provides a subsidy to commuters who adopt a “mixed mode,” which involves combining public transit with hailing rides to/from a transit station. The government funds the subsidy by imposing congestion fees on personal vehicles entering the city center. In an indirect mechanism, instead of levying congestion fees, the government secures funding for the subsidy from the private sector. We examine the implications of both mechanisms on relevant stakeholders. These two mechanisms are especially relevant because several jurisdictions in the United States have begun piloting incentive programs, in which commuters receive subsidies for ride-hailing trips that begin or end at a transit station. Methodology: We present a game-theoretic model to capture the strategic interactions among five self-interested stakeholders (commuters, public transit agency, ride-hailing platform, municipal government, and local private enterprises). Results: By examining equilibrium outcomes, we obtain three key findings. First, we characterize how the optimal interventions associated with the direct or the indirect mechanism depend on: (a) the coverage level of the public transit network; (b) the public transit adoption target; and (c) the relative strength of commuter preferences between driving and taking public transit. Second, we show that the direct mechanism cannot be budget-neutral without undermining commuter welfare. However, when the public transit adoption target is not too aggressive, we find that the indirect mechanism can increase both commuter welfare and sales to the private-sector partner while remaining budget-neutral. Finally, we show that, although the indirect mechanism restricts the scope of government intervention (by eliminating the congestion fee), it can dominate the direct mechanism by leaving all stakeholders better off, especially when the adoption target is modest. Managerial implications: Our findings offer cost-effective prescriptions for improving urban mobility and public transit ridership.

Analyzing Prospective Owners’ Choice Decision towards Plug-in Hybrid Electric Vehicles in Urban India: A Stated Preference Discrete Choice Experiment

Plug-in Hybrid Electric Vehicles (PHEVs) can help decarbonize road transport in urban India. To accelerate the diffusion of PHEVs, investigation of commuter preferences towards the attributes of PHEVs is necessary. Therefore, the present study analyzes prospective owners’ choice decisions towards PHEVs in a typical Indian context. A stated preference survey was designed to collect responses from the current owners of conventional vehicles (CVs) in Delhi, India, and Mixed Logit (ML) models were developed to estimate commuters’ Willingness To Pay (WTP) for a set of key PHEV-specific attributes. The decomposition effect of prospective owners’ sociodemographic characteristics and trip characteristics on the mean estimates of random parameters was investigated by developing ML models with heterogeneity. Subsequently, the influence of improvement of each PHEV-specific attribute on prospective owners’ choice probability was investigated by calculating marginal effects. Among the various PHEV-specific attributes considered in the present study, high WTPs are observed for decrease in battery recharging time, reduction in tailpipe emission and increase in electric range. Therefore, an added emphasis on these attributes by vehicle manufacturers is likely to enhance the attractiveness of PHEVs to Indian commuters. The results also highlight the importance of government subsidy for promoting PHEVs in the Indian market. Prospective owners’ income, availability of home-based parking space, and average daily trip length are found to significantly influence the choice decision of Indian commuters towards PHEVs.

Preferences of public transit commuters: Evidence from smart card data in Singapore

This study employs an administrative dataset containing high-frequency transaction records for approximately four million smart transit cards used by Singaporean residents in order to study the travel preferences of public transport commuters. We examine the impact of service attributes, including travel time, reliability and travel cost, on commuters’ transportation mode choices and estimate the implied value of travel time (VOT) and value of reliability (VOR) for different types of public transit commuters. The results show significant heterogeneity in the transport preferences of commuters, with adult transit commuters having higher VOT and VOR relative to senior citizens, students and children. Within the adult group, commuters who frequently switch their transportation modes have significantly higher VOT and VOR. These results have important implications for policymakers in formulating strategies to improve the efficiency of the public transport system, and having flexible and customized transport services could increase the utility and satisfaction levels of public transit commuters.

Partnerships in Urban Mobility: Incentive Mechanisms for Improving Public Transit Adoption

Problem Definition: Due to a prolonged decline in public transit ridership over the last decade, transit agencies across the United States are in financial crisis. To entice commuters to travel by public transit instead of driving personal vehicles, municipal governments must address the ``last mile'' problem by providing convenient and affordable transportation between a commuter's home and a transit station. This challenge raises an important question: Is there a cost-effective mechanism that can improve public transit adoption by solving the last mile problem? Academic / Practical Relevance: In this paper, we present and analyze two incentive mechanisms for increasing commuter adoption of public transit. In a direct mechanism, the government provides a subsidy to commuters who adopt a ``mixed mode, which involves taking public transit and hailing rides to/from a transit station. The government funds the subsidy by imposing congestion fees on personal vehicles entering the city center. In an indirect mechanism, instead of levying congestion fees, the government secures funding for the subsidy from the private sector. We examine the implications of both mechanisms on relevant stakeholders. These two mechanisms are especially relevant because several jurisdictions in the U.S. have begun piloting incentive programs in which commuters receive subsidies for ride-hailing trips that begin or end at a transit station. Methodology: We present a game-theoretic model to capture the strategic interactions among five self-interested stakeholders (commuters, public transit agency, ride-hailing platform, municipal government, and local private enterprises). Results: By examining equilibrium outcomes, we obtain three key findings. First, we characterize how the optimal interventions associated with the direct or the indirect mechanism depend on: (a) the coverage level of the public transit network; (b) the public transit adoption target; and (c) the relative strength of commuter preferences between driving and taking public transit. Second, we show that the direct mechanism cannot be budget neutral without undermining commuter welfare. However, when the public transit adoption target is not too aggressive, we find that the indirect mechanism is budget neutral, and it can increase both commuter welfare and sales to the private sector partner. Finally, we show that, although the indirect mechanism restricts the scope of government intervention (by eliminating the congestion fee), it can dominate the direct mechanism by leaving all stakeholders better off, especially when the adoption target is modest. Managerial Implications: Our findings offer cost-effective prescriptions for improving urban mobility and public transit ridership.

Commuting and wellbeing: a critical overview of the literature with implications for policy and future research

ABSTRACTThis review provides a critical overview of what has been learnt about commuting’s impact on subjective wellbeing (SWB). It is structured around a conceptual model which assumes commuting can affect SWB over three time horizons: (i) during the journey; (ii) immediately after the journey; and (iii) over the longer term. Our assessment of the evidence shows that mood is lower during the commute than other daily activities and stress can be induced by congestion, crowding and unpredictability. People who walk or cycle to work are generally more satisfied with their commute than those who travel by car and especially those who use public transport. Satisfaction decreases with duration of commute, regardless of mode used, and increases when travelling with company. After the journey, evidence shows that the commute experience “spills over” into how people feel and perform at work and home. However, a consistent link between commuting and life satisfaction overall has not been established. The evidence suggests that commuters are generally successful in trading off the drawbacks of longer and more arduous commute journeys against the benefits they bring in relation to overall life satisfaction, but further research is required to understand the decision making involved. The evidence review points to six areas that warrant policy action and research: (i) enhancing the commute experience; (ii) increasing commute satisfaction; (iii) reducing the impacts of long duration commutes; (iv) meeting commuter preferences; (v) recognising flexibility and constraints in commuting routines and (vi) accounting for SWB impacts of commuting in policy making and appraisal.

Development of neuro‐fuzzy‐based multimodal mode choice model for commuter in Delhi

Delhi is highly plagued by traffic congestion and is notoriously known for its traffic jams. Thus, the question of studying the mode-choice preferences of commuters in Delhi will be integral to travel demand forecasting. The study area poses a challenge in terms of heterogeneity in different types of travel modes available as well as commuters with heterogeneous backgrounds. It offers the typical mix traffic situation prevalent in developing countries, which is cumbersome to model. Eight modes of travel have been considered in this study, which is difficult to come across in previous studies found in the literature. This study proposes to capture mode-choice preferences of commuters by using an adaptive-neuro-fuzzy classifier (ANFC) with linguistic hedges. The proposed mode-choice model will have improved `distinguish-ability' in terms of less overlapping amongst classes, so that the prediction ability is highly improved. Artificial neural network, fuzzy-logic and multinomial-logit models have also been used for analysing mode-choice behaviour of commuters in Delhi. This study is based on microdata collected through household survey conducted in the study area. Results depict that mode-choice model developed by ANFC performs superior to the other models in terms of prediction accuracy.

Public Transit Riderss Preferences: Evidence from Smart Card Transactions in Singapore

This study employs an administrative dataset containing high-frequency transaction records for approximately four million smart transit cards used by Singaporean residents in order to study the travel preferences of public transport commuters. We examine the impact of service attributes, including travel time, reliability and travel cost, on commuters’ transportation mode choices and estimate the implied value of travel time (VOT) and value of reliability (VOR) for different types of public transit commuters. The results show significant heterogeneity in the transport preferences of commuters, with adult transit commuters having higher VOT and VOR relative to senior citizens, students and children. Within the adult group, commuters who frequently switch their transportation modes have significantly higher VOT and VOR. These results have important implications for policymakers in formulating strategies to improve the efficiency of the public transport system, and having flexible and customized transport services could increase the utility and satisfaction levels of public transit commuters.

Urban Transportation Mode Choice and Trip Complexity: Bicyclists Stick to Their Gears

The benefits of public policies designed to alleviate traffic congestion, reduce pollution, and improve public health rest heavily on the value that commuters place on time and how they make transportation mode decisions. However, over the last several years, commuter lives have become more complicated with rising income. This has impacted transportation decisions. In my model, transportation mode choices are made simultaneously with the choice of whether or not to make multiple stops (termed travel complexity). Unlike other studies, I account for commuters' unobserved preferences for particular transportation modes. Using travel behavior data collected by the Oregon Department of Transportation, I estimate the model using an error components logit (ECL) specification and find that not allowing for unobserved commuter preferences for particular transportation modes underestimates value of travel time (VOT) and over-predicts complex trip choice relative to the crossing-components ECL. The estimated model predicts that increased trip complexity causes substitution away from public transit to automobiles, with the exception that bicyclists transition only to more complex trips and do not change transportation modes.

Integration of a Capacity-Constrained Workplace Choice Model: Recent Developments and Applications with an Agent-Based Simulation in Singapore

Destination choice models can be embedded in transport and land use models to understand travel and location choice behavior and to forecast scenarios. Utility-maximizing destination choice models can account for individual behavior and make them suitable for agent-based models, while processing destination capacities is also in line with agent-based modeling. This paper addresses the possibility and impact of introducing capacity constraints, their effect on choice behavior, and the feasibility of applying an approach like this in agent-based microsimulations with individual characteristics for each agent. Here, a comprehensive workplace choice model and its application in a large-scale simulation case study for Singapore are described; one technical and one methodological achievement are highlighted. Technical achievement benefits from recent computational advances; the workplace choice model is estimated with a comprehensive utility function on a large data set with 103 destinations. Reasonable model fit and robust parameters are achieved while obviating sampling techniques; resulting parameters are efficiently applied to the entire 5.4 million Singapore population and validated with survey data. For methodological innovation, capacity limitations are introduced at workplaces to avoid oversaturation. A robust optimization method based on shadow prices is proposed to accommodate capacity limitations at all workplaces during the choice model application defined above. The proposed method efficiently assigns commuters to unused workplaces while respecting individual commuter preferences. Validation of the simulation results, by comparing travel time distributions for commuting trips reported in travel diary data, shows that the model fits well with observed data.

Spatial variation of transit service quality preferences in Dar-es-Salaam

Commuter preferences for transit service quality are of great importance to transit service providers and regulatory agencies. The point of view of potential passengers is fundamental for evaluating transit service quality as they are envisaged to be the real consumers of the planned services and can therefore be considered the most suitable judges. A stated preference survey of daily commuters to the central business district (CBD) in the city of Dar-es-Salaam, Tanzania, was carried out to elicit preferences towards the proposed Dar Rapid Transit (DART) service. Preferences towards the proposed DART service are considered to vary based on the residential location of the potential users. These preferences are evaluated on the attributes travel time, fare and comfort. A binary logit model was applied to establish utility functions that were analysed spatially in a GIS, where a utility value was computed for each spatial unit. This paper accordingly analyses spatial variation of transit service quality preferences for the proposed DART service using geo-spatial techniques. The study uses a scenario-based approach to evaluate the effect of the proposed DART service by comparing the ‘without DART’ scenario, a case representing the existing public transport (daladala), and a ‘DART’ scenario, representing the future proposed DART system. The results indicate that the proposed DART attributes considered in the study have a significant effect on DART service quality. The results further reveal that utility values are significantly varying spatially, particularly dependent upon distance to CBD. Comfort is the most important attribute in zones up to 5km from the CBD, travel fare is more important in zones between 5 and 15km from the CBD, while travel time is more important in zones beyond 15km distance from the CBD. The overall results indicate that the DART is likely to be more attractive than the existing public transport system.

Identifying commuter preferences for existing modes and a proposed Metro in Sydney, Australia with special reference to crowding

In 2009, the New South Wales government announced that it would be proceeding with a feasibility study to identify the patronage potential of a new Metro rail system for Sydney. As part of this study, a new modal choice study was undertaken to establish the role of traditional attributes such as travel times and costs (and more recently, reliability) but also somewhat neglected influences such as crowding, where the later has a critical role in the calculation of capacity needs at railway stations. This paper focuses on the commuter segment and develops a new stated choice experiment in which travellers are able to compare the proposed new Metro with existing available modal alternatives for access, linehaul and egress trip stages, with a particular emphasis on the incorporation of crowding represented by the availability of a seat vs. standing in existing and new public transport modes. We present the error component choice model together with estimates of mode-specific willingness to pay for travel time components, service frequency and crowding, that latter expressed in terms of the probability of getting a seat and the probability of avoiding standing.