In-vehicle Travel Time Research Articles

Multiple shared mobility services under competition: Empirical evidence for public acceptance and policy insights to sustainable transport

Traffic congestion and roadside emissions are severe and common problems in metropolitans. As a promising and sustainable solution to mitigating these vehicle externalities, shared mobility reduces the required vehicle fleet size for serving a given level of demand by sharing a vehicle among travelers with similar schedules and itineraries. Public acceptance is the key to the success of shared mobility development. This study investigates the acceptance of drivers and passengers of two typical competing shared mobility modes, car-pooling and taxi ride sharing, taking Hong Kong as a case study. For an empirical evaluation, an on-street stated preference survey was conducted, and 829 respondents, including 257 private car owners and 572 non-private car owners were interviewed about their travel preferences in three given hypothetical scenarios. In total, 2,487 observations were collected for calibrating two proposed logit-based discrete choice models for drivers and passengers. The model results show that the out-of-pocket cost, in-vehicle travel time, and out-of-vehicle time are key factors influencing travelers’ decisions toward car-pooling and taxi ride-sharing. An equilibrium model was proposed and an iteration solution procedure was applied to obtain a convergent solution to balance the demand and supply of drivers and passengers for car-pooling services. Furthermore, sensitivity analyses were carried out to examine the effects of variations in proportions of travel cost and taxi fare shared by passengers for car-pooling and taxi ride-sharing, and to assist in the formulation of relevant transport policies.

Assessing bus user’s perception using structural equation modelling

ABSTRACT This paper evaluates the user’s perceived quality of the service of a city bus and bus rapid transit (BRT) provided in the study areas of Navi Mumbai and Surat, India. The Structural Equation Modelling (SEM) technique was used to identify the significant variables and quantify the effect of those variables on overall satisfaction. The models were validated using validation indices such as Comparative Fit Index (CFI), Normed Fit Index (NFI), Goodness of Fit Index (GFI), and Cronbach’s alpha which are generally adopted for testing SEM models. Predicted models were observed to represent the actual data to a good extent. It was observed that users consider bus stop waiting time, bus on-time performance, and in-vehicle travel time as most important and riding comfort, safety, and end-to-end connectivity as relatively less important. The outputs provide policymakers with an understanding of the user’s perception of the overall service quality of the transit system.

Characterizing spatiotemporal patterns of bus bunching frequency on a bus route network: A case study of Taipei city

Bus bunching is a critical issue in bus operation management. This study investigated the impacts of potential factors affecting bus bunching on a city-wide bus route network, using Taipei City as an example with its 267 bus routes and 4741 bus stops. Frequency of bus bunching was measured on an hourly basis using Automatic Vehicle Location data. Multilevel count models based on Conway-Maxwell Poisson distributions were developed, and the spatiotemporal distributions of random intercepts and residuals were examined. The results showed that the effects of bus lane design, bus route network and operation, and traffic characteristics were consistent with those reported in prior studies. However, a quadratic relationship between bus route length, best measured by number of stops, and bus bunching was identified, which explains the conflicts between the theories of bus bunching propagation and self-repair. Bus route design often faces a dilemma between extending route length to collect additional passengers and reducing in-vehicle travel time to enhance service quality. The findings of the study could be a useful reference for bus route design to minimize bus bunching.

Topological assessment of recoverability in public transport networks

Reducing the impact of disruptions is essential to provide reliable and attractive public transport. In this work, we introduce a topological approach for evaluating recoverability, i.e., the ability of public transport networks to return to their original performance level after disruptions, which we model as topological perturbations. We assess recoverability properties in 42 graph representations of metro networks and relate these to various topological indicators. Graphs include infrastructure and service characteristics, accounting for in-vehicle travel time, waiting time, and transfers. Results show a high correlation between recoverability and topological indicators, suggesting that more efficient networks (in terms of the average number of hops and the travel time between nodes) and denser networks can better withstand disruptions. In comparison, larger networks that feature more redundancy can rebound faster to normal performance levels. The proposed methodology offers valuable insights for planners when designing new networks or enhancing the recoverability of existing ones.

Modeling non-compensatory strategies on path choices in a complex urban rail transit network considering characteristics of transfer passengers and trips

The path choice behavior of passengers is highly affected by transfer across different lines in a complex urban rail transit network that provides passengers with multiple path alternatives. Hence, the design of a transfer station in terms of its layout and transfer paths is gaining impact on passengers’ path choices in an expanding urban rail transit network. A comprehensive understanding of passenger behavior is essential for transportation planning and management to better design transfer stations and network operations. A questionnaire survey is conducted for passengers’ path choices and perception of transfer stations. Based on the collected data, this study develops a joint path choice model that combines Multinomial Logit Model (MNL) and Constrained Multinomial Logit Model (CMNL) to capture both compensatory and non-compensatory behavior, especially the latter one, which has not been fully explored in the current literature. Further, a membership model is constructed to analyze the heterogeneity of passengers’ non-compensatory behavior on transfer walking time. Four behavioral segments are identified in regard to passenger and trip characteristics. The estimation results show that the proposed model outperforms the general MNL model and allows a more convincing explanation of urban rail transit passenger behavior. Several research findings are derived, including that transfer walking time is more negatively perceived by passengers without non-compensatory behavior. Moreover, in-vehicle travel time is likely to be a minor consideration for passengers with non-compensatory behavior. The proposed model and framework enable urban rail transit operators and planners to retrieve more precise prediction of passenger flows and enhance service quality.

Effects of proactive and reactive health control measures on public transport preferences of passengers – A stated preference study during the COVID-19 pandemic

The outbreak of COVID-19 has caused unprecedented disruptions to public transportation services, passengers' mode choice preferences, and their behavioral pattern. Recent studies have updated the systemic shift from shared travel to private mode due to the perceived higher health risk of shared mobility. However, findings from these studies may not be applicable to public transport-oriented cities with very low rate of private car ownership. In the long term when we arrive at a stabilized ‘New Normal’, the need to promote public transport is likely to be re-emphasized with high priority. This study presents insights into the factors affecting public transport (PT) preferences and potential modal shifts within various modes of PT when proactive and reactive health control measures are implemented. An online stated preference survey was conducted in Hong Kong, where public buses, public light buses, MTR, and taxis are the popular modes of public transport. The regret-based panel mixed multinomial logit model was applied in this study, aiming to capture individual’s regret aversion psychology in the context of the COVID-19 pandemic. The results of our random regret model suggest that travelers generally have lower regrets for the chosen mode when having passenger temperature screening, COVID tracing app, and more frequent disinfection, while seem to be unaware of the in-vehicle air quality. Interaction effects between health control measures and personal characteristics (e.g., age and risk attitudes) were also considered. Trip characteristics, including in-vehicle crowding level, fares, in-vehicle travel time, waiting time at a station, walking time, and trip purpose, were found to influence passengers' choices of travel modes. Policy simulations of possible changes in public transport modal preferences in the presence of various health control measures have also been undertaken. The findings suggest that overall health control measures are appreciated by passengers and do align with promotion of a return to public transport use. However, their effects are quite moderate and should be selectively used for different transport modes. Increasing service frequency can be a promising solution, which reduces crowding and waiting time of passengers using public transport modes, and reduces virus transmission risk. This study contributes to a better understanding of public acceptance and preference toward health control measures in public transport, and call for in-depth cost-benefit analysis in the related fields so that better response can be made in possible future public health crisis.

Dynamic Demand-Responsive Feeder Bus Network Design for a Short Headway Trunk Line

Recent advancements in technology have increased the potential for demand-responsive feeder transit services to enhance mobility in areas with limited public transit access. For long rail headways, feeder bus network algorithms are straightforward, as the maximum feeder service cycle time is determined by rail headway, and bus–train matching is unnecessary. However, for short rail headways, the algorithm must address both passenger–feeder-bus and feeder-bus–train matching. This study presents a simulated annealing (SA) algorithm for flexible feeder bus routing, accommodating short headway trunk lines and multiple bus relocations for various stations and trains. A 5 min headway rail trunk line example was utilized to test the algorithm. The algorithm effectively managed bus relocations when optimal routes were infeasible at specific stations. Additionally, the algorithm minimized total costs, accounting for vehicle operating expenses and passenger in-vehicle travel time costs, while considering multiple vehicle relocations.

A Multi-Objective Mathematical Programming Model for Transit Network Design and Frequency Setting Problem

In this study, we propose a novel multi-objective nonlinear mixed-integer mathematical programming model for the transit network design and frequency setting problem that aims at designing the routes and determining the frequencies of the routes to satisfy passenger demand in a transit network. The proposed model incorporates the features of real-life transit network systems and reflects the views of both passengers and the transit agency by considering the in-vehicle travel time, transfers, waiting times at the boarding and transfer stops, overcrowding and under-utilization of vehicles, and vehicle fleet size. Unlike previous studies that simplify several aspects of the transit network design and frequency setting problem, the proposed model is the first to determine routes and their frequencies simultaneously from scratch, i.e., without using line and frequency pools while considering the aforementioned issues, such as transfers and waiting. We solve the proposed model using Gurobi. We provide the results of what-if analyses conducted using a real-world public bus transport network in the city of Kayseri in Türkiye. We also present the results of computational tests implemented to validate and verify the model using Mandl benchmark instances from the literature. The results indicate that the model produces better solutions than the state-of-the-art algorithms in the literature and that the model can be used by public transit planners as a decision aid.

The multi-vehicle dial-a-ride problem with interchange and perceived passenger travel times

The Dial-a-Ride Problem (DARP) introduced in the early 1980s is the NP-Hard optimization problem of developing the most cost-efficient vehicle schedules for a number of available vehicles that have to start from a depot, pick up and deliver a set of passengers, and return back to the same depot. DARP has been used in many modern applications, including the scheduling of demand-responsive transit and car pooling. This study departs from the original definition of DARP and it extends it by considering an interchange point where vehicles can exchange their picked-up passengers with other vehicles in order to shorten their delivery routes and reduce their running times. In addition to that, this study introduces the concept of generalized passenger travel times in the DARP formulation which translates the increased in-vehicle crowdedness to increased perceived passenger travel times. This addresses a key issue because the perceived in-vehicle travel times of passengers might increase when the vehicle becomes more crowded (i.e., passengers might feel that their travel time is higher when they are not able to find a seat or they are too close to each other increasing the risk of virus transmission or accidents). Given these considerations, this study introduces the Dial-a-Ride Problem with interchange and perceived travel times (DARPi) and models it as a nonlinear programming problem. DARPi is then reformulated to a MILP with the use of linearizations and its search space is tightened with the addition of valid inequalities that are employed when solving the problem to global optimality with Branch-and-Cut. For large problem instances, this study introduces a tabu search-based metaheuristic and performs experiments in benchmark instances used in past literature demonstrating the computation times and solution stability of our approach. The effect of the perceived passenger travel times to the vehicle running costs is also explored in extensive numerical experiments.

Sustainability analysis framework for on-demand public transit systems

There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times.

Correlation analysis and regression of transit ridership by means of public buses – A preliminary data analysis

Given ever-increasing private transportation ownership, a rising population, and unceasing mobility, it is crucial to ensure the usage and improvement of public transportation services. Therefore, it is important to review and understand relationships between variables affecting ridership to boost them. This paper acts as a preliminary data analysis in which correlation and multiple linear regression (MLR) analyses were constructed to examine bus transit ridership. The main goal of this study is to identify the variables that influence transit ridership and the degree to which they do so during a) peak hours and b) off-peak hours. Independent variables such as bus commercial speed, service frequency, in-vehicle travel time, bus stop distance, and rainfall were employed in this study. Results show that all the independent variables are significant and correlated with ridership. However, the regression models show that distance and travel time are not statistically significant during peak and off-peak hours, respectively. The findings of this study recommend the creation of relevant policy initiatives that can assist transit agencies in increasing ridership by analysing significant predictive factors. Additionally, the output from this study can be used in future works on designing efficient bus routes, stop locations, and even bus schedules.

Crowding externalities and optimal subsidies in public transport: Revisiting the Parry–Small model

Subsidising public transport is a widely used policy, normally justified by redistributional objectives. Frequent use of subsidies, however, does not mean uniformity, as the actual subsidy rate varies between countries and cities. Excessive subsidisation of public transport can also be criticised by the presence of externalities, most importantly on-board crowding. This paper revisits Parry & Small (2009), one of the most influential contributions in the recent history of public transport economics. Despite the popularity of the Parry–Small model, it neglects existing empirical evidence on the effects of crowding on passengers’ wellbeing. The purpose of the present study is to redesign and recalibrate their model by expressing crowding disutility as a multiplier of the value of in-vehicle travel time, and also to extend the analysis to the context of the Hungarian interurban transport system. Our results show that the crowding externality is a critical component of optimal pricing, as it decreases the optimal subsidy rate by 4 to 29%, even at a moderate level of capacity utilisation. This means that optimal fares are higher than they were previously found. We extend the original analysis to the case of Hungarian interurban transport and highlight the model’s sensitivity to the assumed substitution pattern.

Empowering revealed preference survey with a supplementary stated preference survey: demonstration of willingness-to-pay estimation within a mode choice case

Mode choice models play a pivotal role in transport demand modelling and help transport planners, engineers and researchers with policy and infrastructure investment evaluation. Recent mode choice studies primarily use revealed preference (RP) data to reflect individuals’ true behaviour. However, this may not be the best practice, given the lack of information in RP data. This study uses a nonlinear utility specification for a multinomial logit mode choice model development using high-quality travel data collected by a GPS-based smartphone application complemented by stated preference (SP) data. The model results highlight the impact of sociodemographic variables on mode choice behaviour and individuals’ willingness-to-pay (WTP) when the model is jointly developed compared to stand-alone SP and RP models. The main message of this study is that in addition to collecting RP, which is a reliable and unbiased source of data, collecting complementary SP data is beneficial as it provides information that is not otherwise available in RP data. This may include a proper variation in the public transport cost variable as demonstrated in this study. Moreover, to better understand the travellers' behaviour regarding the trade-off between time and cost a mixed multinomial logit (MMNL) model in the willingness to pay space is developed on the SP data. capturing the unobserved heterogeneity within the estimated WTPs, the MMNL model outputs reveal a higher variation in WTP of car in-vehicle travel time compared to bus in-vehicle travel time.

A novel modelling approach of integrated taxi and transit mode and route choice using city-scale emerging mobility data

The modelling of mode and route choices of public transport passengers is an essential component of travel demand modelling and transportation planning. Traditionally, choice models are trained using data from revealed and stated preference surveys which are not only cost and time intensive but also suffer from bias due to limitations in sample size. In this article, we present a novel approach utilizing a combination of emerging data sources for non-private modes, namely public transit smart card data, taxi GPS trajectory data, and taxi trips transaction data in order to calibrate an integrated taxi and transit mode and route choice model. We apply our approach for a case study of the taxi and public transport system of Singapore using data sets obtained from the local transport authorities. We solve the first mile and last mile data gaps by overlaying the individual transport nodes against geospatial land use data in order to identify their actual origin and destination points of each journey. To model the behavioural inter-dependencies between the choice of taxi and transit options, we tested a two-level nested logit model and a cross-nested logit model. Along with the commonly included exploratory variables such as in-vehicle travel time, transfer time and number of transfers, our model also incorporates differences in travel cost and separate mode specific constants for peak and off-peak periods. Generic and mode-specific in-vehicle travel time and cost coefficients are tested in the utility functions for the transit and taxi alternatives. Willingness to pay estimates are calculated and compared against similar estimates from Singapore. We also present an application of the model by predicting the mode split between taxi and transit under selected transit and taxi fare change scenarios. Our modelling methodology is highly generalizable and can be applied to other cities with similar data availability.

Travel Behaviour Analysis and Exploration of Mode Choice Variability of Short-Distance Travellers

Commuters tend to shift from public to private transport modes due to various reasons. This results in an increased traffic volume in the urban road network. The ultimate consequence is traffic congestion which creates massive economic losses and adverse environmental pollution. To provide a feasible solution for the above problems, this study is set to examine the factors affecting transport mode choice. Factor analysis was used to identify the factors influencing the mode choice variability of the commuters. Parameter estimation is done using the Multinomial Logit (MNL) model based on the utility maximization theory. The adjusted likelihood ratio index is used to show the model fits. Also, t-statistics and the respective sign of parameters are used to show the validity of estimated parameters. The outcome of the factor analysis shows that age and occupation significantly affect the public transport mode choice under personal characteristics. For private mode choice, the contributing factors are gender, age, occupation and monthly income. Trip distance, invehicle travel time, and travel cost are significantly influencing both public and private transport mode choices. Results of the estimated parameters and elasticity analysis suggest that more commuters can be attracted to public transport by reducing the total travel time of buses.

Mixed fleets of automated and human-driven vehicles in public transport systems: An evaluation of feeder line services

This study focuses on the transitioning period of operating mixed fleets of both automated and human-driven vehicles for public transit services. The type of service investigated here is flexible, including elements of both fixed route and on-demand systems. The operation of the mixed fleet is optimized with analytical methods leading to models for optimal service headway and stop spacing for the two types of vehicles. Analytical models for optimal passenger capacity per vehicle and required fleet size for each type of vehicles are also derived. Four operational strategies are considered, referring to whether the two types of vehicles operate jointly or independently in terms of optimal service headway and stop spacing within the mixed fleet. Numerical analyses indicate that automated vehicles operate optimally with less frequent vehicle dispatches and more fixed stop locations compared to human-driven vehicles. They also require greater fleet size and similar passenger capacity per vehicle. The four operational strategies perform similarly in terms of total generalized costs for the input values considered here. However, sensitivity analyses showed that the operational characteristics of the two types of vehicles in a mixed fleet and the performance of the four operational strategies depend significantly on the percentage of total demand that each type of vehicle serves, as well as on the automated vehicles’ speed and in-vehicle travel time cost for users. The mixed fleets represent the transitioning period towards transit fleets of automated vehicles only and it is shown to be the costliest period for both users and operators.

Value of travel time savings of autonomous vehicle commuters: a segmented valuation for local and inter-city travel

ABSTRACT In this study we aim to identify how the adoption of autonomous vehicles (AVs) affects the value of travel time savings (VOTTS) for commuting trips under different trip distance scenarios (local and inter-city travel). Using a stated preference survey administered to Korean commuters in 2019, two multinomial logit (MNL) mode choice models are developed. These models are stratified by trip length and include four alternatives: AV with a manual driving option (AVMD), AV with a self-driving option (AVSD), shared AV (SAV), and public transit (PT). The results show that the value of in-vehicle travel time savings for AVSD (on average, $7.61/hr) is lower than for the other two AV modes (AVMD = $10.26/hr, SAV = $13.67/hr). It suggests that travelers tend to pay less to reduce travel time while using a private hands-free mode because it allows travelers to use their travel time more productively and/or relieve stress for driving. In addition, changes in the VOTTS for each mode vary by trip length. As travel distance increases, VOTTS for shared modes (SAV and PT) tends to decrease. The value of in-vehicle travel time for SAV in the local travel scenario is the highest among the four modes but is considerably lower than that in the inter-city scenario. These differences suggest that travelers require a certain amount of time to fully use their in-vehicle time, highlighting the importance of trip length when determining the VOTTS. Interestingly, the VOTTS for PT is lower than three AV modes, implying that it might be too early to expect substantial reduction in the VOTTS for AVs due to potential risks of accidents, unfamiliarity with AVs, or immature technology of AVs. Highlights Likelihood of choosing autonomous vehicles is modelled based on trip length. Changes in value of travel time savings for each mode vary by trip length. Value of travel time savings for public transit is lower than autonomous vehicles. Self-driving vehicles have a low value of travel time savings than other modes. People need a certain amount of time to fully perform activities while traveling.

Estimating suburban preferences for integrated public transit and ridesourcing services through a revealed-preference/stated-preference survey

Low-density areas like suburbs can be challenging to serve with fixed-route transit. Demand responsive transit has been used to service these areas with mixed results, but suburban travellers may respond to ridesourcing integrated with transit more positively because of the improved booking and dispatching interface offered by ridesourcing. This paper explores how suburban residents perceive transit-integrated ridesourcing, both for cost and time attributes of the system and in comparison to other travel modes in the area (cycling, auto, taxi or Uber, and transit). A Hierarchical Bayes mixed logit model was estimated using the results of a revealed-preference/stated-preference survey, revealing that transit-integrated ridesourcing had similar preferences to fixed-route transit. Across time and cost variables, respondents were more sensitive to walk time, transfer time, the number of transfers, parking cost, and taxi or Uber fare, and were minimally impacted by changes in the IVTT deviation of their trip times. The change in utility for the length of transit or transit-integrated ridesourcing in-vehicle travel time versus auto in-vehicle travel time was found to have a strongly linear relationship, while other attributes exhibited non-linearity to different degrees, particularly walk time, transfer time, transit-integrated ridesourcing fare, and taxi or Uber fare. Respondents who were female, younger, taking home-based school trips, or had lower household incomes were more open to transit-integrated ridesourcing and transit than other demographic groups. The likelihood of taking each of the five travel modes was assessed after COVID-19, finding generally minimal to negative impact for transit-integrated ridesourcing. Insights into vehicle and transit pass ownership, existing travel alternatives, and a prior transit-integrated ridesourcing pilot in the area are presented.

Assessing public transport loyalty in a car-dominated society: The case of Luxembourg

Public transport (PT) systems face the challenge of retaining users and preventing a shift towards individual transport modes. While satisfaction is recognized as a key factor in user loyalty, there is a need to understand the specific PT attributes that contribute to passenger satisfaction and foster loyalty. This study aims to assess the impact of PT service attributes on user loyalty, controlling for socio-demographic characteristics. Data from an online survey conducted in the Grand Duchy of Luxembourg, a country with high car dependency, were analysed using logistic regression models. The findings highlight the importance of attributes such as reliable service, in-vehicle travel time, number of transfers, and feeling safe, while also identifying differences in attribute importance between bus and train loyalty. The study provides valuable insights for transport agencies and policymakers to enhance user loyalty and develop effective ridership retention strategies. These findings are particularly relevant in the post-pandemic scenario and can contribute to addressing car dependency challenges in diverse metropolitan areas. The paper concludes with policy recommendations to improve PT services based on the identified attributes.

Evaluating skip-stop policy in urban rail transit systems based on passenger cost

Increasing the operating speed in public transport systems can increase the system capacity, reduce the overall passenger travel time and improve experienced comfort. Skip-stop operation, where subsets of the trains operating on the same tracks skip certain intermediate stops, can accelerate the service and improve passengers’ overall travel experience. This paper considers the problem of deciding whether skip-stop operation is beneficial for a given line and which stopping scheme is the most effective. In particular, we investigate whether a simple decision rule for determining the stopping pattern under a skip-stop strategy, derived from the expected weighted time benefits to the passengers, can reliably determine the most suitable skip-stop scheme. To evaluate the impact of alternative stop-skipping strategies, we adopt the existing public transit assignment model BusMezzo, which allows for a realistic representation of passengers’ experienced waiting and in-vehicle travel times and the resulting trade-offs between passenger costs and benefits. The decision rule is applied to a set of high-frequency urban rail lines in Stockholm, Sweden. We show that a simple decision rule may not be a robust way of determining a beneficial skip-stop scheme. The results from the case study reveal that the skip-stop operation can have an overall positive impact on passenger generalized travel time but only under certain conditions at the stops along the line.