Value Of Travel Time Research Articles

Influence of Winter Weather Conditions on Value of Travel Time

Influence of Winter Weather Conditions on Value of Travel Time

Impacts of an economic incentive for intercity peer-to-peer ride-sharing in sparsely populated areas

Abstract Ride-sharing can increase the accessibility of areas with limited transport options, such as rural areas. However, drivers may not be willing to detour from their route to collect passengers in low-demand areas. We analyse the introduction of an economic incentive to an intercity ride-sharing platform to encourage drivers to stop in small towns along their route. The results of a simulation analysis using a synthetic network show that social welfare is maximised when the economic incentive is about 3.3 times the value of travel time when demand and supply are in equilibrium. However, individual welfare gains were small. When passengers placed a high value on travel time, the incentive had a negative impact on social welfare. The incentive always had a negative impact on the income of the platform provider.

Departure Time and Route Choices With Accurate Information Under Binary Stochastic Bottleneck Capacity in the Morning Commute

Uncertainty, a critical factor of causing congestion and extra travel costs in the commute, can be mitigated by providing information. This paper studies the welfare effects of accurate pre-trip information on departure time and route choices in the morning commute under binary stochastic bottleneck capacity. We consider a classical two-route network. Each route has a single bottleneck where congestion occurs during the rush hours. The two routes’ bottleneck capacities vary from day-to-day due to events such as bad weather, accidents, and temporary road closures. We derive all equilibrium solutions in consideration of the differences between routes in free-flow travel time, the shadow value of travel time, the severity of bottleneck capacity reductions, and the degree of correlation between two routes in travel conditions. Furthermore, we investigate the benefit changes from zero-information to full-information and prove that accurate pre-trip information about the bottleneck conditions is strictly welfare-improving. Finally, these theoretical results are supplemented by case studies that show examples of benefit gains from pre-trip information.

Economic Evaluation of Traffic Congestion at Intersection: Case study from an Indian City

Traffic congestion causes negative impacts to transport sector and cause a massive increase in the transportation cost. Congestion cost evaluation helps wider aspects of the policy and planning and thus by providing potential solutions to the traffic congestion problem. This study evaluates the traffic congestion cost at signalized intersections located in the state of Kerala, India which prevail heterogeneous traffic conditions. Congestion cost estimations are done by considering the factors including delay, traffic volume, passenger occupancy, and value of travel time of different vehicle types. The annual congestion cost of traffic congestion at each approach of signalized intersections is quantified. The mode share of the present study reveals that private vehicles such as two wheeler and car constitute an average share of 75% of total traffic and which has the maximum share of traffic compared to public vehicles. Therefore, this study considers a candid relief measure proposal as mode shift to public transport from private vehicles. The result shows that marginal change in the two wheeler shift to public transport in Indian city reduce the congestion cost by 38% and thus can promote usage of public transport in the Indian cities to reduce congestion.

Measuring travel time reliability and risk: A nonparametric approach

The reliability of travel time significantly affects individual travelers’ decision-making behaviour and hence in turn influences the overall travel demand at the macroscopic level. The travel time reliability ratio (TTRR), defined to be the ratio of the value of travel time variability to the value of travel time, is an important metric for measuring such reliability. In this paper, we first show that the TTRR is closely related to a widely used risk measure in financial economics, i.e. conditional value at risk (CVaR). Then based on the connection between the TTRR and CVaR, we develop a nonparametric approach to estimate the TTRR. In the literature, to compute the TTRR, it usually needs to assume a specific statistical distribution for the travel time. This can produce a misleading result when this assumption goes awry due to the potential complexity of travel time distributions. Based on the relationship between the TTRR and CVaR, this paper proposes a new nonparametric method, i.e. the kernel density estimation method, to overcome this problem. We show that this new nonparametric method is robust in terms that it does not depend on any assumptions about the shape of the travel time distribution. The simulation studies demonstrate that the proposed method outperforms the existing methods and substantially improves the numerical accuracy. Finally, a practical example is used to illustrate the proposed method.

Anticipating long-distance travel shifts due to self-driving vehicles

Increasing population and travel demand has prompted new efforts to model travel demand across the United States. One such model is rJourney that estimates travel demand among thousands of regions and models mode and destination choice. rJourney includes records representing 1.17 billion long-distance trips throughout the year 2010. Although inter-regional impacts caused by an increase of automated vehicles (AVs) has been investigated, there is little research on inter-regional travel and how longer distance destination and mode choices will change. Because of conveniences offered by AVs, the value of travel time of drivers is expected to fall, thus reducing the generalized cost of AV travel. To initially analyze the impacts of AVs in the United States, a new AV mode was added to a subset of the rJourney mode and destination choice models. With an initial scenario assuming an operating cost of AVs that is 118% of traditional cars, two outcomes are observed that are solely based on model results. First, the attractiveness of AVs severely digs into the airline travel market, reducing airline revenues to 53%. Second, the introduction of AVs results in a shift of destination choice, increasing travel in further distances for personal vehicles, but favoring closer distances across all modes, for an overall 6.7% decline in US passenger-miles traveled on existing long-distance trips. While this preliminary research has revealed an initial perspective on how an existing model can support AVs, the increasing availability of data as AVs emerge will refine nationwide long-distance modeling.

Autonomous driving and residential location preferences: Evidence from a stated choice survey

The literature suggests that autonomous vehicles (AVs) may drastically change the user experience of private automobile travel by allowing users to engage in productive or relaxing activities while travelling. As a consequence, the generalised cost of car travel may decrease, and car users may become less sensitive to travel time. By facilitating private motorised mobility, AVs may eventually impact land use and households’ residential location choices. This paper seeks to advance the understanding of the potential impacts of AVs on travel behaviour and land use by investigating stated preferences for combinations of residential locations and travel options for the commute in the context of autonomous automobile travel. Our analysis draws from a stated preference survey, which was completed by 512 commuters from the Sydney metropolitan area in Australia and provides insights into travel time valuations in a long-term decision-making context. For the analysis of the stated choice data, mixed logit models are estimated. Based on the empirical results, no changes in the valuation of travel time due to the advent of AVs should be expected. However, given the hypothetical nature of the stated preference survey, the results may be affected by methodological limitations.

Evaluating reliability of municipal public transport operation in the Russian Federation cities

The article presents the results of a study of municipal public passenger transport routes operation reliability. For the first time in Russian practice, a range of Travel Time and Buffer indices values is identified based on route networks of two different cities. A scale to evaluate route reliability levels is proposed.

What value do travelers put on connectivity to mobile phone and Internet networks in public transport? Empirical evidence from the Paris region

Unlike the drivers of private vehicles, public transport (PT) users may perform secondary tasks during their primary travel activity. Moreover, Information and Communication Technologies may open up “multi-tasking” possibilities by allowing individuals to spend their travel time in more pleasant ways. This article proposes a tentative valuation of connectivity to mobile phone and Internet networks (MPIN) in PT, based on the stated preferences of 501 inhabitants of the Paris region. The surveyed individuals were presented with hypothetical trade-offs between travel time reductions and improvements in MPIN connectivity in PT. Econometric tests show that the values ascribed to better connectivity are higher when PT users perform various tasks with smartphones or tablets during their trips and when they experience a large number of connectivity problems in the reference situation. While heterogeneity between individuals has a minor direct impact, we propose a typology of PT users that captures variations in valuations. On average, the subjective value of travel time would be reduced by 12% if PT users benefited from optimal MPIN connectivity whilst traveling. Alternative “time multipliers” – for types of PT user, gradual connectivity improvements, different device-based tasks – are also proposed. Lastly, we apply our results to a cost-benefit analysis of a current project in the Paris region PT.

Newton-type Gauss–Seidel Lax–Friedrichs high-order fast sweeping methods for solving generalized eikonal equations at large-scale discretization

We propose a Newton-type Gauss–Seidel Lax–Friedrichs sweeping method to solve the generalized eikonal equation arising from wave propagation in a moving fluid. The Lax–Friedrichs numerical Hamiltonian is used in discretization of the generalized eikonal equation. Different from traditional Lax–Friedrichs sweeping algorithms, we design a novel approach with a line-wise sweeping strategy. In the local solver, the values of traveltime on an entire line are updated simultaneously by Newton’s method. The global solution is then obtained by Gauss–Seidel iterations with line-wise sweepings. We first develop the Newton-based first-order scheme, and on top of that we further develop high-order schemes by applying weighted essentially non-oscillatory (WENO) approximations to derivatives. Extensive 2-D and 3-D numerical examples demonstrate the efficiency and accuracy of the new algorithm. The combination of Newton’s method and Gauss–Seidel iterations improves upon the convergence speed of the original Lax–Friedrichs sweeping algorithm. In addition, the Newton-type sweeping method manipulates data in a vectorized manner so that it can be efficiently implemented in modern programming languages that feature array programming, and the resulting advantages are extremely significant for large-scale 3-D computations.

Enjoy the most beautiful scene now: a memetic algorithm to solve two-fold time-dependent arc orienteering problem

Traditional route planners commonly focus on finding the shortest path between two points in terms of travel distance or time over road networks. However, in real cases, especially in the era of smart cities where many kinds of transportation-related data become easily available, recent years have witnessed an increasing demand of route planners that need to optimize for multiple criteria, e.g., finding the route with the highest accumulated scenic score along (utility) while not exceeding the given travel time budget (cost). Such problem can be viewed as a variant of arc orienteering problem (AOP), which is well-known as an NP-hard problem. In this paper, targeting a more realistic AOP, we allow both scenic score (utility) and travel time (cost) values on each arc of the road network are time-dependent (2TD-AOP), and propose a memetic algorithm to solve it. To be more specific, within the given travel time budget, in the phase of initiation, for each population, we iteratively add suitable arcs with high scenic score and build a path fromthe origin to the destination via a complicate procedure consisting of search region narrowing, chromosome encoding and decoding. In the phase of the local search, each path is improved via chromosome selection, local-improvement-based mutation and crossoveroperations. Finally, we evaluate the proposed memetic algorithm in both synthetic and real-life datasets extensively, and the experimental results demonstrate that it outperforms the baselines.

Modelling preferences towards activities and their effect on departure time choices

A great challenge that comes with cities’ growth and urbanization is how to transport their inhabitants efficiently. Since available urban space has been progressively reducing, the attention has shifted from capacity investment to transport demand management. As a result, policies aimed at changing departure time choices have arisen. To properly analyze such policies, it is necessary to understand how and when people prefer to travel, which is a result of the activities individuals perform. This paper studies preferences towards activities to be conducted and the impact of these preferences on departure time choices. For this purpose, a stated preference survey was developed to reproduce a daily activity pattern for all respondents. With the collected data it was possible to compare alternative approaches to model activities proposed in the literature. This comparison shows that traditional formulations to model activities preferences might not be the ideal in some cases. Instead, the use of more complex formulations, such as sigmoidal functions, is encouraged since they could adequately represent the preferences regarding activity's duration and time of day, as well as activity's flexibility. Results show that activities to be conducted before and after each trip affect the departure time choices and should be considered when modelling and forecasting travel demand. Therefore, a proper benefit estimation from travel time savings should include a direct and indirect component: one related to avoid travelling (which is an undesired activity by itself) and another one related to the increase of available time to perform activities instead of travelling. Our results show that if the latter effect is not included, the value of mean travel time would be underestimated by 38% and total benefits could be miscalculated.

Would being driven by others affect the value of travel time? Ridehailing as an analogy for automated vehicles

It is widely believed that vehicle automation will change how travelers perceive the value of travel time (VoTT), but the magnitude of this effect is still unknown. This study investigates how highly automated vehicles (AVs) may affect VoTT, using an existing mode—ridehailing services (RHS)—as an analogy for AVs. Both AVs and RHS relieve travelers from the effort of driving and allow them to participate in other activities while traveling. In a stated choice experiment, respondents chose between driving a personal vehicle or taking an RHS, with each mode characterized by a cost and travel time. Analysis results using a mixed logit model indicated that the VoTT was 13% lower when being driven in an RHS than when driving a personal car. We also told half the respondents (randomly selected) that the RHS was driverless; and for half (also randomly selected) we explicitly mentioned the ability to multitask while traveling in an RHS. Mentioning multitasking explicitly led to a much lower VoTT, approximately half that of driving oneself. However, the VoTT in a driverless RHS was 15% higher than when driving a personal car, which may reflect a lack of familiarity and comfort with driverless technology at present. These results suggest sizable reductions in VoTT for travel in future AVs, and point to the need for caution in making forecasts based on consumers’ current perceptions of AV technology.

Analyzing effects of transport policies on travelers’ rational behaviour for modal shift in Denmark

In this paper, an innovative agent-based model is developed to simulate emergent patterns arising from individual actions to analyze opportunities for modal shift in Denmark (ABMoS-DK). Themodeling processsimulates the interaction between travelers (agents) and the network, and applies aheuristic algorithm to model travelers’rational decision making process based on tangible costs (ticket price, fixed and variable operation and maintenance costs) and Value of Travel Time (VOTT) explained by Value of Time (VoT), travel time and level of service. The traveler is described by a set of socio-economic attributes (income, family structure, place of residence, car/bike ownership) and the utility is derived from properties of alternative modes to determine whether to use non-motorized, public or private transport. Fluctuation of tangible costs and value of travel time can provide comparative advantages to the alternative modes and has the potential to affect the utility of the mode derived by traveler and change mode choice decision. A set of “pull” and “push” policy scenarios are formulated to help us understand how different factors affect mode choice in transport. We find that disincentivizing private cars has the highest potential for shifting from car use followed by incentives for sustainable modes and expansion of public transport infrastructure. The paper concludes that capturing rational behavioural features of consumers with fine level of heterogeneity in modelling will help to better understand the dynamics of the transportation system and consequently assist policy makers to better identify and target consumer groups with the highest shift potential.

Valuation of travel time in free-flow and congested traffic and its reliability-estimates for Czech Republic

A prerequisite for any monetary estimation of economic losses arising from traffic congestion is a reliable estimate of the value of travel time. Although the available evidence from abroad reveals significant differences in the values of individual components of time travel, specific valuations of travel time are rather rare in the Czech Republic. The aim of this paper is to expand the data base in the Czech Republic by presenting selected estimates of the travel time components for Czech drivers and passengers. Specifically, we elaborate on the value of travel time and time spent in congestion for medium-long domestic routes (represented by the Prague-Brno route). The results of our valuation study (N = 602) show that the value of travel time spent in congestion is almost twice as high as the value of travel time in free-flow traffic and suggest that the reduction of congestion and of the variation in travel time in general will bring significant economic benefits compared to shortening travel time in free-flow traffic. This evidence confirms the importance of distinguishing between different components of travel time when assessing the benefits of transport infrastructures. Importantly, our estimates of the value of travel time are much lower than those officially endorsed by Czech Ministry of Transport (MoT) for analysing benefits of new transport infrastructure.

Bus Travel Time Prediction Model Based on Profile Similarity.

In road-based mass transit systems, travel time is a key factor in providing quality of service. This article proposes a method of predicting travel time for this type of transport system. This method estimates travel time by taking into account its historical behaviour, represented by historical profiles, and the current behaviour recorded on the public transport vehicle for which the prediction is to be made. The model uses the k-medoids clustering algorithm to obtain historical travel time profiles. A relevant feature of the model is that it does not require recent travel time data from other vehicles. For this reason, the proposed model may be used in intercity transport contexts in which service planning is carried out according to timetables. The proposed model has been tested with two real cases of intercity public transport routes and from the results obtained we may conclude that, in general, the average error of the predictions is around 13% compared to the observed travel time values.

The Individual Travel Cost Method with Consumer-Specific Values of Travel Time Savings

The treatment of the opportunity cost of travel time in travel cost models has been an area of research interest for many decades. Our analysis develops a methodology to combine the travel distance and travel time data with respondent-specific estimates of the value of travel time savings (VTTS). The individual VTTS are elicited with the use of discrete choice stated preference methods. The travel time valuation procedure is integrated into the travel cost valuation exercise to create a two-equation structural model of site valuation. Since the travel time equation of the structural model incorporates individual preference heterogeneity, the full structure model provides a travel cost site demand model based upon individualized values of time. The methodology is illustrated in a study of recreational birdwatching, more specifically, visits to a ‘stork village’ in Poland. We show that the usual practice of basing respondents’ VTTS on 1/3 of their wage rate is largely unfounded and propose alternatives—including a separate component of the travel cost survey aimed at valuation of respondents’ VTTS or, as a second best, asking if they wish if their journey was shorter and for those who do—use full hourly wage as an indicator of their VTTS.

What will autonomous trucking do to U.S. trade flows? Application of the random-utility-based multi-regional input–output model

This study anticipates changes in U.S. highway and rail trade patterns following widespread availability of self-driving or autonomous trucks (Atrucks). It uses a random-utility-based multiregional input–output model, driven by foreign export demands, to simulate changes in freight flows among 3109 U.S. counties and 117 export zones, via a nested-logit model for shipment or input origin and mode, including the shipper’s choice between autonomous trucks and conventional or human-driven trucks (Htrucks). Different value of travel time and cost scenarios are explored, to provide a sense of variation in the uncertain future of ground-based trade flows. Using the current U.S. Freight Analysis Framework (FAF4) data for travel times and costs—and assuming that Atrucks lower trucking costs by 25% (per ton-mile delivered), truck flow values in ton-miles are predicted to rise 11%, due to automation’s lowering of trucking costs, while rail flow values fall 4.8%. Rail flows are predicted to rise 6.6% for trip distances between 1000 and 1500 miles, with truck volumes rising for all other distance bands. Introduction of Atrucks favors longer truck trades, but rail’s low price remains competitive for trade distances over 3000 miles. Htrucks continue to dominate in shorter-distance freight movements, while Atrucks dominate at distances over 500 miles. Eleven and twelve commodity sectors see an increase in trucking’s domestic flows and export flows, respectively. And total ton-miles across all 13 commodity groups rise slightly by 3.1%, as automation lowers overall shipping costs.

The value of travel time, noise pollution, recreation and biodiversity: A social choice valuation perspective

Environmental effects of transport projects have a weak position in Cost-Benefit Analysis (CBA) which might be rooted in the valuation approach adopted in the dominant style of CBA. This conventional valuation approach has been criticized for not valuing positive and negative impacts of transport projects in relation to each other and for not valuing such impacts in a public context, but in the context of private decisions. These critiques might be circumvented through valuing transport projects in a social choice context in which overall burdens and benefits of proposed transport projects are considered together in a public context. We investigate the extent to which a social choice valuation approach produces different outcomes than a conventional valuation approach. We conducted four social choice valuation experiments in which respondents were asked to choose between alternatives for a new road, trading off travel time and three environmental impacts (noise, recreation and biodiversity). Our findings suggest that, under social choice valuation, individuals assign substantially more value to environmental impacts than travel time as compared to conventional valuation studies. Moreover, in a social choice setting, respondents assigned monetary values to impacts that are not (or only qualitatively) considered in conventional CBAs of transport projects.

Modelling long-distance route choice using mobile phone call detail record data: a case study of Senegal

The growing mobile phone penetration rates have led to the emergence of large-scale call detail records (CDRs) that could serve as a low-cost data source for travel behaviour modelling. However, to the best of our knowledge, there is no previous study evaluating the potential of CDR data in the context of route choice behaviour modelling. Being event-driven, the data are discontinuous and only able to yield partial trajectories, thus presenting serious challenges for route identification. This paper proposes techniques for inferring the users' chosen routes or subsets of their likely routes from partial CDR trajectories, as well as data fusion with external sources of information such as route costs, and then adapts the broad choice framework to the current modelling scenario. The model results show that CDR data can capture the expected travel behaviour and the derived values of travel time are found to be realistic for the study area.