In-vehicle Travel Time Research Articles

The Influence Change of Private Car Ownership on Patterns of Mode Choice in Baghdad city

The growth rate of vehicle ownership in Baghdad has increased since 2003. For the analysis of adverse effects of car ownership on transport mode choice, the comparison between the patterns of mode choices in the past with current situation was studied. The transport mode choice was calibrated using a Logit model; it combines different characteristics of the alternatives and individual socioeconomic variables in a meaningful way to calculate a utility value. The model requires data of zones, choice data of travelling mode and the selected mode attributes. Trans Cad was used to calibrate the utility functions, MNL analysis in Trans Cad is used to develop the model. The travelling modes used in the study area are private mode (car) and public mode (bus). The level of service variables (In-vehicle travel time, cost) and socioeconomic variables (auto ownership, income) are used. After calibration of the split model, the process of validation is achieved by comparing field measured modal split of traffic volumes with estimated split results of the model. The results show that a higher share of public transport in the past, while in 2014, it appears that the share of auto is higher. Through these results, it finds that public transport must be given the utmost importance in order to provide a sustainable environment in the region.

Multi-constrained bus holding control in time windows with branch and bound and alternating minimization

This work proposes a periodic bus holding control method where the bus holding times of all running trips are computed simultaneously within each optimization time period; thus, increasing the coordination among running buses for avoiding bus bunching. This paper considers the adverse effects of the bus holding control in the in-vehicle travel times of on-board passengers and performs holistic bus holding decisions by modelling the bus holding problem as a discrete, nonlinear, constrained optimization problem. Given the computational complexity of the bus holding problem, an alternating minimization approach is introduced for computing the optimal holding times at each optimization instance. The performance of the periodic control method is evaluated against the performance of event-based control methods using 5-month automated vehicle location and automated passenger count data from bus line 1 in Stockholm for contacting simulation-based experiments.

Impact of Automated Vehicles on Travel Mode Preference for Different Trip Purposes and Distances

Because of technology penetration in the transportation system, the automated vehicle (AV) is set to be a future mode of transport. Given the major implications of AVs, investigation of the potential impact of these vehicles on travel behavior is vital for a wide range of purposes, especially for policy making. In this study, we report the results of a stated preference survey distributed in the Netherlands in which respondents had to choose between conventional cars, public transportation, and AVs for different travel distances and trip purposes. Having collected information from 663 respondents we conducted an integrated study incorporating classic trip attributes (such as travel time and travel costs), attitudinal factors, and socio-economic variables to understand people’s choices. We studied a particular form of AVs, automated driving transport service (ADTS), which we defined as an automatically controlled door-to-door transport service provided by a vehicle with similar dimensions to a conventional car, albeit driverless. Results suggest that travelers’ mode preferences vary significantly for different travel distances and purposes. We found that conventional cars and public transportation are perceived as being the least attractive alternatives in relation to in-vehicle travel time on short- and long-distance commuting trips, respectively. Preference for ADTS lay between the car and public transportation, neither the best nor the worst alternative in all scenarios. Our findings suggest that ADTS adopters are likely to prefer this mode for long-distance leisure trips rather than short-distance commuting trips.

Mechanism design for first-mile ridesharing based on personalized requirements part I: Theoretical analysis in generalized scenarios

Ridesharing is an effective transportation mode to provide first-mile accessibility to public transit and a low-cost, environment-friendly, and sustainable mobility service. This paper designs a mechanism for the first-mile ridesharing service. The mechanism accounts for passengers’ personalized requirements on different inconvenience attributes (e.g. the number of co-riders, extra in-vehicle travel time, and extra waiting time at the transit hub) of the service in determining the optimal vehicle-passenger matching and vehicle routing plan and customized pricing scheme. The proposed mechanism is proved to be individual rational, incentive compatible, and price non-negative. The three properties respectively indicate that passengers are willing to participate in the service, that honestly reporting personalized requirements is the optimal strategy, and that the service provider is guaranteed to receive revenue from the participants. A case study is proposed to interpret the mechanism and to demonstrate the generality of the personalized-requirement-based mechanism that can be adapted into different scenarios.

A Two-Stage Algorithm for School Bus Stop Location and Routing Problem With Walking Accessibility and Mixed Load

This paper proposes a School Bus Stop location and Routing Problem with Walking Accessibility and Mixed Load (SBSLRP-WA-ML), where the individual difference of walking accessibilities among students and the possibility of serving students attending different schools with the same bus simultaneously are considered. We first develop a mixed integer programming model for SBSLRP-WA-ML with the objective of minimizing the total commuting time, including walking time from the residence to school, in-vehicle travel time, and service time at stops. A two-stage solution method is then developed. In stage 1, an iterative clustering method based on k-means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to locate bus stops aiming at minimizing the number of stops subject to various walking accessibilities. In stage 2, an improved ant colony optimization algorithm (IACO) integrating two local search operators is devised, which is used to generate bus routes with minimal total commuting time. A number of instances of different sizes are generated to verify the solution approach, and the influential factors with respect to total commuting time are analyzed. The model is also compared to the door-to-door school bus services. Comparison to similar methods and sensitivity analysis of parameters are also conducted to analyze the performance and robustness of the proposed approach.

A stochastic congested strategy-based transit assignment model with hard capacity constraints

Abstract This paper expands on the stochastic version of the strategy-based congested transit assignment problem described in Codina and Rosell (2017b), specifically by extending it to cases dealing with explicit line capacities. Following this previous work, the stochastic model with explicit line capacities takes into account the stochastic mean waiting times and in-vehicle travel times of passengers at stops during the standard scenario journey of the planning period. This slightly modified model is formulated also as a stochastic variational inequality derived from the deterministic version of the problem with similar properties and characteristics, formulated by the authors in a previous work. For this stochastic capacitated model, two algorithms are presented which may take advantage of simplified simulation models for the bus stops in order to provide realistic mean waiting times for passengers at stops. Both algorithms can be easily adapted to the case of the model with non-strict capacities by Codina and Rosell (2017b).

Commuter travel cost estimation at different levels of crowding in a suburban rail system: a case study of Mumbai

This research values travel attributes such as waiting time, in-vehicle time and crowding levels using behavioural data obtained from Mumbai local train commuters through a stated preference experiment. Actual on-board crowding images are considered to perceive the crowding more realistically by the train users. A multinomial logit modelling technique is used for estimating commuter travel cost (time) at different crowding levels. Results show that there is an increase in perceived in-vehicle travel time with the increase in crowding level. Traveling in a crowded seating condition increases the travel cost by 0.81 min per 1 min travel. A crowded seat leads the user to perceive an 81% increase in in-vehicle travel time, whereas this perception increases by 282% more during travel in super dense crush crowding compared to normal travel conditions. The generalized travel cost increases to a maximum in the super dense crush crowding. An effect analysis was carried out to understand the sensitivity of gender, age, income, and trip length on travel attributes. Female users tend to perceive more decrease in utility due to crowding than male users. A reduction of 60% in seating capacity will lower the perceived travel cost per minute by 43% during peak hours of travel. Essentially, a reduction in seat capacity will increase the standee capacity which in turn offers more comfort to standees while standing in super dense crush load condition. Presented discussions in this research are important to policy-makers and planners in the Mumbai Railway Vikas Corporation to monitor, measure and develop programs for the local train operation service quality. The study findings will be useful for developing a policy framework to deal with issues related to the level of service improvement for the suburban rail system in India and other developing economies.

The total social costs of constructing and operating a maglev line using a case study of the riyadh-dammam corridor, Saudi Arabia

Background: Introducing Magnetic Levitation (Maglev) technology in a developing country is a big challenge that needs huge investments in infrastructure, operations and maintenance. Background information about the development of Maglev worldwide to date is included.
 Aim: Determine a methodology to estimate the full cost of travel and provide insights into the working model developed to include the calculations of the total social costs of building a new Maglev line for the Riyadh-Dammam corridor in Saudi Arabia and understand in what circumstances it is a suitable technology to use.
 Methods: The Spreadsheet Total Cost Model (STCM) is used to determine the calculations of operator costs, user costs, and environmental costs. However, the operator costs are related to infrastructure construction and maintenance costs, and costs associated with the acquisition, operation and maintenance of Maglev rolling stock. The user costs are dependent on the journey time, including access/egress time, waiting time, and in-vehicle time. The value of time is considered in order to get the user costs calculated. The external costs include air pollution, noise pollution, accident, and climate change per passenger-km.
 Results: The travel demand has to be forecasted in order to determine the total social costs, using the elasticity approach between the the proposed HSR and Maglev lines in terms of their number of trips and the generalised journey times. In addition, the generalised journey time is based on the in-vehicle time and service interval penalty. The Maglev system is operated at the capacity limit and the change in service is therefore forecasted to increase the Maglev demand by 24.6 % (3.25 million passengers). In terms of the total infrastructure costs, the infrastructure construction and maintenance costs are included and computed to be about € 835.4 million per year, using the capital recovery factor (0.06) based on 35 years of operation and a 5 % social discount rate. The acquiring, operating, and maintaining train’s unit cost is included in the calculation of rolling stock to achieve results of € 22.4 million, € 22.5 million, and € 40.9 million, respectively. In terms of the user costs, the access/egress time is computed as of 33 minutes, using the car, while the in-vehicle travel time and waiting time are resulted of 61.8 minutes and 7.8 minutes, respectively. The external environmental costs are based on accidents, and climate changes of € 8.87 million per year and € 8.13 million per year, respectively. However, the total social costs of Maglev line are computed as € 1.18 billion for 16.45 million passengers per year. This gives an average social cost of € 71.9 per passenger. The comparable figures for High-Speed Rail (HSR) are € 1.10 billion for 13.21 million passengers per year, giving an average social cost of € 83 per passenger.
 Conclusion: In conclusion, the Riyadh-Dammam Maglev system introduces a new intercity system into Saudi Arabia and brings new competition in the intercity transit market as a part of the future transport developments in the country. The average social cost for HSR is around 16 % higher than Maglev- but that is more proven technology.

Access mode choice to low-cost airports: Evaluation of new direct rail services at Milan-Bergamo airport

The purpose of this study is to investigate air passengers' choice of the access mode at low-cost airports, with the aim of supporting policy makers in evaluating improvements to the current ground access transport system. We assess the impact of the introduction of a new direct rail service on the airport's accessibility by passengers, relying on revealed preference data collected at the Milan-Bergamo airport in the period 2013–2016. We first implement a mixed logit model to examine the behavior of outgoing passengers and evaluate their sensitivity to various access time components and cost incurred. Second, based on the estimated coefficients, the introduction of direct rail services from/to Milan is assessed by means of a sensitivity analysis. Results show that low-cost airports should be aware of customers' priorities and not simply aim to deliver the solution with the lowest possible cost. The estimated value of time measures—equal to €40/h, €24/h, and €19/h for traffic, out-of-vehicle travel time, and in-vehicle travel time, respectively—reveal that low-cost airline passengers are not exclusively cost-driven when confronted with the access mode choice but do place considerable value on access time savings. This finding is corroborated by the assessment of different rail services, showing how the introduction of an airport express train has the potential to increase train usage much more (+7%) than the extension of the existing regional commuter service (+1%).

What transit service does the periphery need? A case study of Israel’s rural country

Rural and peripheral areas with low population density and long travel distances challenge the supply of public transport service. The objective of this paper is to analyze which type of public transport service best fit the periphery and compare alternative services based on a set of quantities and qualitative measures with emphasize on equity considerations. We applied for the first time the Potential Mobility Index (PMI) developed by Martens (2016) to analyze the equity implications of the alternatives. We applied it in a new and unique way with two measures of travel time: in-vehicle travel time and door to door travel time. The research applies the methodology to a case study of Israel’s peripheral cities. Two different alternative services were analyzed: high-speed rail to the Northern and Southern peripheral cities, and a fix schedule regional BRT shuttle service with a timed transfer to rail hubs near the periphery.The accessibility measures show that the rail service to the periphery provides high accessibility, however, the demand analysis suggests that the demand would be low to justify high-speed rail service. The analysis of the in-vehicle time PMI shows an advantage to the high-speed rail alternative, however, the door to door calculation of PMI shows that the BRT service gives better PMI scores to all the peripheral cities that we examined and can be effective in providing a high quality of service to the periphery. The results provide some insights for research and service planning in rural areas and found some generalization to equity aspects in the periphery dilemma.

A Modeling Analysis of Impact from E-Hailing Service on Non-Work Travel Mode in Shanghai, China

With the rapid development of mobility services, e-hailing service have been highly prevalent and e-hailing travel has become a part of daily life in many cities in China. At the same time, travelers’ mode choice behaviors have been influenced to some degree by different factors, and in this paper, a web-based retrospective survey initially conducted in Shanghai, China is used to analyze the extent to which various factors are influencing mode choice behaviors. Then, a multinomial-logit-based mode choice model is developed to incorporate the e-hailing auto mode as a new travel mode for non-work trips. The developed model can help to identify influential factors and quantify their impact on mode choice probabilities. The developed model involves a variety of explanatory variables including e-hailing/taxi fare, bus travel time, rail station access/egress distance, trip distance, car in-vehicle travel time as well as travelers’ socioeconomic and demographic characteristics, etc. The model indicates that the e-hailing fare, travel companions and some travelers’ characteristics (e.g., age, income, etc.) are significant factors influencing the choice of e-hailing mode. The alternative-specific constant in the e-hailing utility equation is adjusted to match the observed market share of the e-hailing mode. Based on the developed model, elasticities of LOS attributes are computed and discussed. The research methods used in this paper have the potential to be applied to investigate travel behavior changes under the influence of emerging travel modes. The research findings can aid in evaluating policies to manage e-hailing services and improve their levels of services.

Integrating ridesourcing services with public transit: An evaluation of traveler responses combining revealed and stated preference data

Inspired by the success of private ridesourcing companies such as Uber and Lyft, transit agencies have started to consider integrating ridesourcing services (i.e. on-demand, app-driven ridesharing services) with public transit. Ridesourcing services may enhance the transit system in two major ways: replacing underutilized routes to improve operational efficiency, and providing last-mile connectivity to extend transit’s catchment area. While an integrated system of ridesourcing services and public transit is conceptually appealing, little is known regarding whether and how consumers might use a system like this and what key service attributes matter the most to them. This article investigates traveler responses to a proposed integrated transit system, named MTransit, at the University of Michigan Ann Arbor campus. We conducted a large-sample survey to collect both revealed preference (RP) and stated preference (SP) data and fit a RP-SP mixed logit model to examine the main determinants of commuting mode choice. The model results show that transfers and additional pickups are major deterrents for MTransit use. We further applied the model outputs to forecast the demand for MTransit under different deployment scenarios. We find that replacing low-ridership bus lines with ridesourcing services could slightly increase transit ridership while reducing operations costs. The service improvements offered by ridesourcing mainly come from reductions in wait time. Though relatively small in our study, another source of improvement is the decrease of in-vehicle travel time. Moreover, we find that when used to provide convenient last-mile connections, ridesourcing could provide a significant boost to transit. This finding verifies a popular notion among transit professionals that ridesourcing services can serve as a complement to public transit by enhancing last-mile transit access.

Modelling passenger waiting time using large-scale automatic fare collection data: An Australian case study

Passenger waiting time at transit stops is an important component of overall travel time and is perceived to be less desirable than in-vehicle travel time or access time. Therefore, an accurate model to estimate waiting time is necessary to better plan for transit and to improve patronage. The majority of previous studies on waiting time have either made very limiting assumptions on the arrival distribution of passengers or lacked a large-scale and high-quality dataset. The smartcard fare collection system in South-East Queensland, Australia, has provided the opportunity of very large-scale and highly accurate data on passenger boarding and alighting times and locations. In this research, all 130,000 daily rail passengers in all 145 stations of a network are considered. First a methodology is developed to match each individual passenger with the most likely rail service he/she boarded. Then, a hazard-based duration modelling approach is adapted to model passenger waiting time as a function of a variety of factors that influence waiting time. Log-logistic accelerated failure time (AFT) models are inferred to be appropriate among the models tested. The results indicate that: (a) the waiting time can be predicted accurately at various confidence levels; (b) the waiting time at all network stations can be predicted with a single model; and (c) a wide range of influencing parameters are statistically significant in the model, which can be categorized to temporal, infrastructure and operation, demographics, and trip characteristics parameters. The results of this study can be used for demand estimation, operational analysis, transit scheduling, and network design through an understanding of the effects of influential variables on waiting time.

Reducing passengers’ travel time by optimising stopping patterns in a large-scale network: A case-study in the Copenhagen Region

Optimising stopping patterns in railway schedules is a cost-effective way to reduce passengers’ generalised travel costs without increasing train operators’ costs. The challenge consists in striking a balance between an increase in waiting time for passengers at skipped stations and a decrease in travel time for through-going passengers, with possible consequent changes in the passenger demand and route choices. This study presents the formulation of the skip-stop problem as a bi-level optimisation problem where the lower level is a schedule-based transit assignment model that delivers passengers’ route choices to the skip-stop optimisation model at the upper level, and where the upper level in return provides an improved timetable to the lower level. A heuristic method for large-scale urban networks is presented to solve this extremely complex bi-level problem, where the skip-stop optimisation is a mixed-integer problem, whereas the route choice model is a non-linear non-continuous mapping of the timetable. The method was tested on the suburban railway network in the Greater Copenhagen Region (Denmark): the reduction in railway passengers’ in-vehicle travel time was 5.5%, the reduction in passengers’ generalised travel cost was 3.2% and, at the system level, the yearly consumer surplus amounted at 76.7 million DKK (about 10.3 million EUR or 12.7 million USD) when compared to the existing stopping patterns.

A meta-analysis and synthesis of public transport customer amenity valuation research

ABSTRACTThis paper synthesises published research concerned with the valuation of public transport customer amenities. It includes normalisation of published values to equivalent terms, a meta-analysis of factors influencing these values, a review of valuation methods and issues faced in applying these methods, and an identification of gaps in knowledge. Some 57 separate research publications in this area were identified. Valuation methods adopted included stated preference, revealed preference, customer ratings, priority evaluator, maximum difference scaling and benefit/value transfer. Of these, stated preference was the most common published approach, however in practice benefit/value transfer is the most common method to apply values in project appraisal. Key issues associated with valuation include substantial variation in values (which are often context dependant) and the adoption of different units for expressing values, thereby limiting their transferability. Some 556 separate customer amenity values were identified relating to 97 separate amenity types. Almost all valuations were below 2 minutes of equivalent in-vehicle travel time value. Meta-analysis identified five types of significant predictors of values (R2 = 0.23): study location (Scandinavia), valuation method (stated preference), mode (train/metro), amenity group (access) and journey stage (boarding/alighting). Future research needs to disaggregate valuations by market segment.

Modeling stochastic overload delay in areliability-based transit assignmentmodel

In this paper, we develop a reliability-based transit assignment model with capacity constraints that adopt a new stochastic overload delay formulation. The in-vehicle travel times and waiting times are set to random variables. We adopt the statistical characteristics of the normal distribution to model the stochastic overload delay. The stochastic overload delay gradually varies as the passenger flow changes. A method is developed based on the method of successive averages algorithm to solve the reliability-based transit assignment model. Moreover, numerical experiments are conducted to illustrate the properties of the proposed model and the efficiency of the proposed algorithm.

Learning the route choice behavior of subway passengers from AFC data

This paper learns the route choice behavior of passengers from Auto Fare Collection, timetable, and train loading data using a method combined with Bayesian inference and Metropolis-Hasting sampling. First, the influential factors of route choice such as in-vehicle travel time, transfer time, and in-vehicle crowding are given. Next, formulations are established based on AFC, timetable and train loading data, which are merged into a logit model of route choice behavior of subway passengers. Next, an algorithm integrating Bayesian inference and Metropolis-Hasting sampling is designed to calibrate parameters of the logit model. Finally, a case study of Beijing subway is applied to verify the validity of the model and algorithm. A detailed discussion shows that in-vehicle crowding plays a crucial role in passenger route choice behavior.

Transit Network Design Based on the Bi-Level Programming

In this work, we study the transit network design problem on the basis of present urban road network from the perspective of programming and optimizing. So we propose a bi-level programming model to design the transit network. Considering the generation of bus lines, the number of stops along the lines, the interval of departing time, the number of buses on each line every day and so on, planners make endeavors to minimize running cost of transit system in the upper programming model. The lower level programming model is mainly from the angle of the travelers, taking travel time that is also called general travel cost, including in-vehicle travel time and transfer time, as the optimization goal. Finally, a simple experiment is performed to demonstrate the efficiency of the bi-level model above.

New appraisal values of travel time saving and reliability in Great Britain

This paper provides an overview of the study ‘Provision of market research for value of time savings and reliability’ undertaken by the Arup/ITS Leeds/Accent consortium for the UK Department for Transport (DfT). The paper summarises recommendations for revised national average values of in-vehicle travel time savings, reliability and time-related quality (e.g. crowding and congestion), which were developed using willingness-to-pay (WTP) methods, for a range of modes, and covering both business and non-work travel purposes. The paper examines variation in these values by characteristics of the traveller and trip, and offers insights into the uncertainties around the values, especially through the calculation of confidence intervals. With regards to non-work, our recommendations entail an increase of around 50% in values for commute, but a reduction of around 25% for other non-work—relative to previous DfT ‘WebTAG’ guidance. With regards to business, our recommendations are based on WTP, and thus represent a methodological shift away from the cost saving approach (CSA) traditionally used in WebTAG. These WTP-based business values show marked variation by distance; for trips of less than 20 miles, values are around 75% lower than previous WebTAG values; for trips of around 100 miles, WTP-based values are comparable to previous WebTAG; and for longer trips still, WTP-based values exceed those previously in WebTAG.

Congestion-aware system optimal route choice for shared autonomous vehicles

We study the shared autonomous vehicle (SAV) routing problem while considering congestion. SAVs essentially provide a dial-a-ride service to travelers, but the large number of vehicles involved (tens of thousands of SAVs to replace personal vehicles) results in SAV routing causing significant congestion. We combine the dial-a-ride service constraints with the linear program for system optimal dynamic traffic assignment, resulting in a congestion-aware formulation of the SAV routing problem. Traffic flow is modeled through the link transmission model, an approximate solution to the kinematic wave theory of traffic flow. SAVs interact with travelers at origins and destinations. Due to the large number of vehicles involved, we use a continuous approximation of flow to formulate a linear program. Optimal solutions demonstrate that peak hour demand is likely to have greater waiting and in-vehicle travel times than off-peak demand due to congestion. SAV travel times were only slightly greater than system optimal personal vehicle route choice. In addition, solutions can determine the optimal fleet size to minimize congestion or maximize service.