Value Of Travel Time Research Articles

A heuristic algorithm based on leaf photosynthate transport

Transportation network design is non-deterministic polynomial-time hard due to its attributes of multi-objects, multi-constraints, and the non-convexity objective function. In this paper, a bi-level programming model is proposed for the transportation network design. The upper layer pursues the minimum total travel time of users and the total length of the road network simultaneously, while the lower layer is an equilibrium assignment model. A new algorithm for the network optimization based on the principle of leaf photosynthate transport in nature is proposed. The proposed algorithm simulates the natural selection of biological evolution and genetic transmission. It can retain the genetic idea of the evolutionary algorithm, together with the heuristic information update mechanism of swarm intelligence. Finally, empirical research is carried out with the Sioux Falls network to validate the performance of the proposed algorithm. The results show that although the total network length obtained by the proposed algorithm increases slightly compared with the ant colony algorithm and the genetic algorithm, the total travel time and objective function value reduce obviously. This indicates that the proposed algorithm has good performance on topology and efficiency.

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.

Mode choice with latent availability and consideration: Theory and a case study

Over the last two decades, passively collected data sources, like Global Positioning System (GPS) traces from data loggers and smartphones, have emerged as a very promising source for understanding travel behaviour. Most choice model applications in this context have made use of data collected specifically for choice modelling, which often has high costs associated with it. On the other hand, many other data sources exist in which respondents’ movements are tracked. These data sources have thus far been underexploited for choice modelling. Indeed, although some information on the chosen mode and basic socio-demographic data is collected in such surveys, they (as well as in fact also some purpose collected surveys) lack information on mode availability and consideration. This paper addresses the data challenges by estimating a mode choice model with probabilistic availability and consideration, using a secondary dataset consisting of ‘annotated’ GPS traces. Stated mode availability by part of the sample enabled the specification of an availability component, while the panel nature of the data and explicit incorporation of spatial and environmental factors enabled estimation of latent trip specific consideration sets. The research thus addresses an important behavioural issue (explicit modelling of availability and choice set) in addition to enriching the data for choice modelling purposes. The model produces reasonable results, including meaningful value of travel time (VTT) measures. Our findings further suggest that a better understanding of mode choices can be obtained by looking jointly at availability, consideration and choice.

Capturing value of reliability through road pricing in congested traffic under uncertainty

Empirical studies showed that travel time reliability, usually measured by travel time variance, is strongly correlated with travel time itself. Travel time is highly volatile when the demand approaches or exceeds the capacity. Travel time variability is associated with the level of congestion, and could represent additional costs for travelers who prefer punctual arrivals. Although many studies propose to use road pricing as a tool to capture the value of travel time (VOT) savings and to induce better road usage patterns, the role of the value of reliability (VOR) in designing road pricing schemes has rarely been studied. By using road pricing as a tool to spread out the peak demand, traffic management agencies could improve the utility of travelers who prefer punctual arrivals under traffic congestion and stochastic network conditions. Therefore, we could capture the value of travel time reliability using road pricing, which is rarely discussed in the literature. To quantify the value of travel time reliability (or reliability improvement), we need to integrate trip scheduling, endogenous traffic congestion, travel time uncertainty, and pricing strategies in one modeling framework. This paper developed such a model to capture the impact of pricing on various costs components that affect travel choices, and the role of travel time reliability in shaping departure patterns, queuing process, and the choice of optimal pricing. The model also shows the benefits of improving travel time reliability in various ways. Findings from this paper could help to expand the scope of road pricing, and to develop more comprehensive travel demand management schemes.

Willingness to pay and willingness to accept in a patient-centered blood pressure control study

BackgroundElevated blood pressure is a major risk factor for cardiovascular disease and stroke but patients often discount recommended behavioral changes and prescribed medications. While effective interventions to promote adherence have been developed, cost-effectiveness from the patient’s perspective, has not been well studied. The valuation of patient time and out of pocket expenses should be included while performing cost effectiveness evaluation. The AchieveBP study uses the contingent valuation method to assess willingness to accept (WTA) and willingness to pay (WTP) among patients with a history of uncontrolled blood pressure discharged from an urban emergency department and enrolled in a larger randomized controlled trial.MethodsWTA and WTP were assessed by asking patients a series of questions about time and travel costs and time value related to their study participation. A survey was conducted during the final study visit with patients to investigate the effectiveness of a kiosk-based educational intervention on blood pressure control. All study patients, regardless of study arm, received the same clinical protocol of commonly prescribed antihypertensive medication and met with research clinicians four times as part of the study procedures.ResultsThirty-eight patients were offered the opportunity to participate in the cost-effectiveness study and all completed the survey. Statistical comparisons revealed these 38 patients were similar in representation to the entire RCT study population. All 38 (100.0%) were African-American, with an average age of 49.1 years; 55.3% were male, 21.1% were married, 78.9% had a high school or higher education, and 44.7% were working. 55.9% did not have a primary care provider and 50.0% did not have health insurance. Time price linear regression analysis was performed to estimate predictors of WTA and WTP.ConclusionsWTP and WTA may generate different results, and the elasticities were proportional to the estimated coefficients, with WTP about twice as responsive as WTA. An additional feature for health services research was successful piloting in a clinical setting of a brief patient-centered cost effectiveness survey.Trial registrationhttps://clinicaltrials.gov. Registration Number NCT02069015. Registered February 19, 2014 (Retrospectively registered).

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.

Network-based model for predicting the effect of fuel price on transit ridership and greenhouse gas emissions

Abstract As fuel prices increase, drivers may make travel choices to minimize not only travel time, but also fuel consumption. Consideration of fuel consumption would affect route choice and influence trip frequency and mode choice. For instance, travelers may elect to live closer to their workplace, or use public transit to avoid fuel consumption and the associated costs. To incorporate network characteristics into predictions of the effects of fuel prices, we develop a multi-class combined elastic demand, mode choice, and user equilibrium model using a generalized cost function of travel time and fuel consumption with a combined solution algorithm. The algorithm is implemented in a custom software package, and a case study application on the Austin, Texas network is presented. We evaluate the fuel-price sensitivity of key variables such as drive-alone and transit class proportions, person-miles traveled, link-level traffic flow and per capita fuel consumption and emissions. These effects are examined across a heterogeneous demand set, with multiple user-classes categorized based on their value of travel time. The highest relative transit elasticities against fuel price are observed among low value of time classes, as expected. Although total personal vehicle travel decreases, congestion increases on some roads due to the generalized cost function. Reductions in system-wide fuel consumption and greenhouse gas emissions are observed as well. The study uncovers the combined interactions among fuel prices, multi-modal choice behavior, travel performance, and resultant environmental impacts, all of which dictate the urban travel market. It also equips agencies with motivation to tailor emissions reduction and transit-ridership stimulus policies around the most responsive user classes.

Estimating the Value of Time and Its Application

Values of travel time are essential factors in the design of transport infrastructure. The value of time is used in transport models to monetize travel time related to the socio-economic background of travelers. This paper assesses the estimation of the value of time based on questionnaires distributed to travelers in a preference survey. The mode choice approach is employed to estimate these values dependent on vehicle classes for weekdays and at the weekend. Two of the main conclusions using the mode choice approach are that the value of time for private vehicle users is approximately 1.5 times the value of time for public transport users; and the value of travel time on the weekday is twice that of the weekend. This indicates that public transport passengers have more travel time savings than when they use other modes. Another method, the income approach, arrives at similar values of time as that estimated by the mode choice approach. The willingness to use public transport in weekdays is increasing. As a consequence, public transport should be operated at a higher frequency.

A study on benefit estimation that considers the values of travel time and travel time reliability in road networks

ABSTRACTThis study proposes a benefit estimation method that considers travel time reliability. The proposed method is based on a network model that is formulated as a utility maximization problem with constraints. Since this utility maximization problem has the same equilibrium conditions as a multi-class user equilibrium traffic assignment problem with elastic demand, both transport demand forecasting and benefit estimation can be carried out in the same framework. By assuming a certain form for the utility function, the road network model can estimate the prohibitive price, so the proposed method is convenient for estimating opportunity loss due to disruption of origin–destination connection in the event of a natural disaster. Furthermore, the values of travel time and travel time reliability are estimated endogenously in the proposed method; thus, changes in these values can be reflected in the benefit estimation. A numerical experiment demonstrates the method presented in this study.

Bottleneck congestion and residential location of heterogeneous commuters

This study examines the effects of bottleneck congestion and an optimal time-varying congestion toll on the spatial structure of cities. We develop a model in which heterogeneous commuters choose departure times from home and residential locations in a monocentric city with a bottleneck located between a central downtown and an adjacent suburb. We then demonstrate that commuters sort themselves temporally and spatially on the basis of their value of travel time and their flexibility. Furthermore, we reveal that introducing an optimal congestion toll alters the urban spatial structure, which contrasts with the previous literature. We further demonstrate through an example that congestion tolling can cause the city to physically expand outward, which helps rich commuters but hurts poor 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.

Valuing travel time savings: A case of short-term or long term choices?

The valuation of travel time is of crucial importance in many transport decisions. Most studies make use of data framed around short-term decisions such as route choice. However, people may have a greater ability to trade time and money in a longer term setting, such as when considering changes in residential or employment locations. We study the value of travel time in both the short and long-term, finding differences in the valuations. Given the importance of these valuations for policy making, our results call for more research into how time-cost trade-offs should be represented with stated preference.

Reliability-Based Traffic Signal Control for Urban Arterial Roads

It is widely accepted that travelers value both the reliability of travel time and its mean or expected value. Strategies for traffic signal control typically seek to optimize average travel times, although reliability is in general not explicitly taken into account. In this paper, we propose a new framework for evaluating the consequences of signal-control tactics on both reliability and expected values of travel time, based on an analytic model of travel time distribution. A genetic-algorithm-based approach is then employed to identify optimal multicriteria signal control strategies, including sensitivity analysis, to the relative weighting between reliability and expected value. We expose the properties of the proposed framework via an empirical case study of four alternative optimization approaches (the signal setting optimized with the traditional Webster's method, TRANSYT model, and the newly proposed model) under various traffic conditions. Results indicate that the newly proposed framework outperforms the alternative signal control strategies in terms of both travel-time variability and expected travel time.

국도 단속류 구간에서 DSRC를 활용하여 수집한 개별차량 통행시간의 최적 수집 간격 결정 연구

연구는 국도 단속류 구간에서 DSRC로 수집한 개별차량 통행시간의 대푯값 산정 시 신뢰도를 높이는 최적 수집 간격을 결정하는데 목적이 있다. 이를 위하여, 단속류 구간에서 수집되는 가장 대표적인 개별차량 통행시간의 분포인 양봉형태의 비대칭 분포를 따르는 수집데이터를 활용하고 개별차량 통행시간의 수집 간격 크기를 변화시켜 MSE(Mean Square Error)를 추정함으로 오차가 최소가 되는 최적 수집 간격 크기를 결정한다. MSE 산정을 위한 편의 추정식은 비대칭 분포에서도 활용이 가능한 t-분포의 최대 추정 오차식을 활용하였다. 최적 수집 간격 분석을 위한 데이터 수집 간격은 단속류 구간에서 신호정지로 데이터 수집이 정상적으로 결측 되는 1-2분 수집 간격은 제외하고, 3분 이상의 수집 간격만을 대상으로 하였다. 데이터 수집 시 결측을 발생시키는 수집 간격은 결측 데이터 보정처리 과정에서 또 다른 오차를 유발하게 되어 배제하였다. 분석결과 MSE가 최소가 되는 최적 수집 간격은 3-5분이며, 통행시간 증가 시 최적 수집 간격은 3분으로 짧아짐을 확인하였다. 시스템 운영의 효율성과 통행시간 대푯값 산정의 신뢰도 향상을 모두 고려할 때 기본 수집 간격은 기존과 같이 5분으로 운영하고, 정체 시는 3분으로 수집 간격을 줄여 운영하는 것이 효과적일 것으로 사료된다. The purpose of this study is to determine the optimal aggregation interval to increase the reliability when estimating representative value of individual vehicle travel time collected by DSRC equipment in interrupted traffic flow section in National Highway. For this, we use the bimodal asymmetric distribution data, which is the distribution of the most representative individual vehicle travel time collected in the interrupted traffic flow section, and estimate the MSE(Mean Square Error) according to the variation of the aggregation interval of individual vehicle travel time, and determine the optimal aggregation interval. The estimation equation for the MSE estimation utilizes the maximum estimation error equation of t-distribution that can be used in asymmetric distribution. For the analysis of optimal aggregation interval size, the aggregation interval size of individual vehicle travel time was only 3 minutes or more apart from the aggregation interval size of 1-2 minutes in which the collection of data was normally lost due to the signal stop in the interrupted traffic flow section. The aggregation interval that causes the missing part in the data collection causes another error in the missing data correction process and is excluded. As a result, the optimal aggregation interval for the minimum MSE was 3~5 minutes. Considering both the efficiency of the system operation and the improvement of the reliability of calculation of the travel time, it is effective to operate the basic aggregation interval as 5 minutes as usual and to reduce the aggregation interval to 3 minutes in case of congestion.

Speed Choice, Car Following Theory and Congestion Tolling

This paper provides a link between car following theory and the economic theory of road congestion by means of a theory of speed choice. According to this theory speed choice is based on a trade-off between the benefits (shorter travel time) and cost (higher accident risk) of driving faster. Accident risk is related to the distance to the 'leader' and by elaborating this relationship a number of car-following models can be derived from this theory of speed choice. Wit homogeneous traffic, steady state analysis leads to a model that generalizes the conventional Pigou-Knight analysis: It has an endogenous speed choice curve and requires the incorporation of accident risk in the value of travel time. A further generalization of this model to steady states with heterogeneous traffic is possible and leads to the conclusion that first best tolls will in general require differentiation over groups of drivers. Finally a general bottleneck model is discussed that contains Vickrey's (1969) and Verhoef's (2002) versions as special cases. This results in a clarification of Verhoef's finding that implementation of the optimal Vickrey toll can result in a deterioration of welfare.

Flexing service schedules: Assessing the potential for demand-adaptive hybrid transit via a stated preference approach

This paper assesses the demand for a flexible, demand-adaptive transit service, using the Chicago region as an example. We designed and implemented a stated-preference survey in order to (1) identify potential users of flexible transit, and (2) inform the service design of the flexible transit mode. Multinomial logit, mixed-logit, and panel mixed-logit choice models were estimated using the data obtained from the survey. The survey instrument employed a dp-efficient design and the Google Maps API to capture precise origins and destinations in order to create realistic choice scenarios. The stated-preference experiments offered respondents a choice between traditional transit, car, and a hypothetical flexible transit mode. Wait time, access time, travel time, service frequency, cost, and number of transfers varied across the choice scenarios. The choice model results indicate mode-specific values of in-vehicle travel time ranging between $16.3 per hour (car) and $21.1 per hour (flexible transit). The estimated value of walking time to transit is $25.9 per hour. The estimated value of waiting time at one’s point of origin for a flexible transit vehicle is $11.3 per hour; this value is significantly lower than the disutility typically associated with waiting at a transit stop/station indicating that the ‘at-home’ pick-up option of flexible transit is a highly desirable feature. The choice model results also indicate that respondents who use active-transport modes or public transit for their current commute trip, or are bikeshare members, were significantly more likely to choose flexible and traditional transit than car commuters in the choice experiments. The implications of these and other relevant model results for the design and delivery of flexible, technology-enabled services are discussed.

The Value of Travel Time and Reliability: Empirical Evidence from Katy Freeway

Travelers place value on both time savings and reliability when choosing a route for a trip. The value of travel time (VOT) has long been an integral part of the appraisal of transport projects. Recently, some transport planners have been incorporating the value of travel time reliability (VOR) as well. This research used data generated by automated vehicle identification sensors from Katy Freeway travelers in Houston, Texas, to estimate VOT and VOR based on revealed preferences of these travelers. Lane choice models were developed to examine the factors influencing travelers’ lane choice decisions in different traffic conditions and to estimate their VOT. Models with two independent variables— travel time and toll—resulted in an estimated VOT from $1.96 to $8.06/h for all trips on a monthly basis. The research could not conclude whether the VOR had any impact on travelers’ lane choice decisions. The percentage of managed-lane trips was higher for the those who traveled the whole length of the managed–general-purpose lanes than for those who traveled only a part of the managed–general-purpose lanes. This difference might be the result of easier accessibility at the end of the managed lanes compared with that for midpoints.

Capturing Value of Reliability through Road Pricing in Congested Traffic under Uncertainty

Abstract Empirical studies showed that travel time reliability, usually measured by travel time variance, is strongly correlated with travel time itself. Travel time is highly volatile when the demand approaches or exceeds the capacity. Travel time variability is associated with the level of congestion, and could represent additional costs for travelers who prefer punctual arrivals. Although many studies propose to use road pricing as a tool to capture the value of travel time (VOT) savings and to induce better road usage patterns, the role of the value of reliability (VOR) in designing road pricing schemes has rarely been studied. By using road pricing as a tool to spread out the peak demand, traffic management agencies could improve the utility of travelers who prefer punctual arrivals under traffic congestion and stochastic network conditions. Therefore, we could capture the value of travel time reliability using road pricing, which is rarely discussed in the literature. To quantify the value of travel time reliability (or reliability improvement), we need to integrate trip scheduling, endogenous traffic congestion, travel time uncertainty, and pricing strategies in one modeling framework. This paper developed such a model to capture the impact of pricing on various costs components that affect travel choices, and the role of travel time reliability in shaping departure patterns, queuing process, and the choice of optimal pricing. The model also shows the benefits of improving travel time reliability in various ways. Findings from this paper could help to expand the scope of road pricing, and to develop more comprehensive travel demand management schemes.

Examining methodological issues on combined RP and SP data

Abstract Revealed preference (RP) and stated preference (SP) data have been widely used in transportation studies to understand user's preferences regarding various travel decisions. This paper focuses on investigating the modeling techniques to address various issues associated with pooled estimation using both RP and SP data, such as the scale variances, the user heterogeneity, and the state dependency effect, etc. Various model structures are explored, and the results are compared in terms of the significance level of the key parameters, the model performance, and the implications on the derived values of (Value of travel Time) VOT and (Value Of travel time Reliability) VOR. The model results indicate that when inter-alternative scale parameters are incorporated, the model performance and fit improve significantly, with much more reasonable state dependency factors, while the error components and the standard deviations for the random parameters in Mixed Logit (ML) model lose their influence. It suggests that the inter-alternative scale parameters are able to capture the unobserved heterogeneity across alternatives and individuals and relax the Independence of Irrelevant Alternatives (IIA) of Multinomial Logit (MNL) models. This paper contributes to the literature by adding empirical studies and providing insights on the modeling approaches to handle joined dataset, particularly in the field of examining VOT and VOR based on joint RP and SP survey data.

Endogenous market penetration dynamics of automated and connected vehicles: Transport-oriented model and its paradox

Abstract Automated and connected vehicles (ACVs) have received a great deal of attention. Indeed, their full market penetration will be desirable in terms of traffic efficiency, as ACVs can efficiently drive by precisely and instantaneously communicating, recognizing, and reacting to other ACVs. However, it is not yet certain whether traffic efficiency is improved in mixed traffic where ratio of manual vehicles is substantially high. This is because, for example, ACVs in mixed traffic may require excessive safety clearance, as they have to rely on relatively imperfect vision/radar-based vehicle recognition. Meanwhile, relative benefit of ACVs compared to manual vehicles would be proportional to travel time (because the most significant merit of ACVs for their driver is comfortable in-vehicle experience) and therefore severity of congestion. Consequently, equilibrium states of a myopic car market may suffer severer congestion and higher social cost than the current state—this is congestion paradox. This kind of phenomena can be considered as a consequence of market penetration of a good with network externality or social interaction, where market penetration of ACVs is endogenously determined based on their cost/benefit which depend on current number of ACVs users. This study analyzes this problem under idealized conditions. Specifically, a theoretical model of endogenous market penetration of ACVs considering changes in value of time, travel time, and transportation fare, which are the most direct impacts of ACVs to the society, is formulated. Then, its market dynamics is analyzed. Finally, strategic policies to avoid congestion paradox and achieve social optimum are proposed.