Value Of Travel Time Research Articles

An efficient data-driven method to construct dynamic service areas from large-scale taxi location data

Service area analysis is crucial for determining the accessibility of public facilities in smart cities. However, the acquisition of service areas using conventional approaches has been limited. First, investigating traffic flow is difficult, as this factor varies significantly over time and space. Second, obtaining service areas of mobile facilities/targets has remained a challenge owing to a lack of data and methods. To address these problems, this study proposes an efficient big-data-driven approach that utilizes large-scale taxi GPS location data collected over two years within Seoul City and distributed computation to obtain the average travel time values on fine-grained grid cells of 100 m × 100 m resolution. On-the-fly visualization methods were then established with an ability to construct isochrone maps of service areas in near-real-time. This enabled performing accurate service area analysis of mobile facilities/targets dynamically. The proposed solution can be effectively used in various applications, such as optimizing the ride-sharing services or the routes of autonomous electric vehicles in future smart cities, as demonstrated in this study.

Shared Autonomous Vehicles as Last-Mile Public Transport of Metro Trips

The “last-mile problem” of public transportation is one of the main obstacles affecting travelers who choose to utilize public transport. Although autonomous vehicles (AVs) have made much progress, they have not been officially put into commercial use. This paper adopts stated preference experiments to explore the impact of shared AVs on the last-mile travel behavior of metro users and takes Wuhan as an example for case analysis. First of all, this paper establishes a structural equation model (SEM) based on the theory of planned behavior to explore latent psychological variables, including travelers’ attitudes (ATTs), subjective norms (SNs), perceived behavior control (PBC), and behavioral intention of use (BIU) toward AVs. These latent psychological variables are incorporated into the latent class (LC) logit model to establish a hybrid model with which to study the factors and degree of influence on the travel mode choices of travelers for the last mile of their metro trips. The results show that travelers have preference heterogeneity for the travel mode choices for the last mile of metro trips. Through the analysis of LCs, education, career, and income significantly impact the classification of LCs. The latent psychological variables towards AVs have a significant impact on the travel behavior of respondents, but the impacts vary among different segments. Elastic analysis results illustrate that a 1% increase in the travel cost for shared AVs in segment 1 leads to a 7.598% decrease in the choice probability of using a shared AV. Respondents from different segments vary significantly in their willingness to pay for their usage, and the value of travel time for high-income groups is relatively higher.

Transformer Graph Convolutional Network for Relative Travel-Time Shift Prediction

Abstract Predicting surface-wave travel-time shifts is valuable for analyzing potential effects caused by changes in medium properties, station clock errors, instrument response errors, and other factors. Many current neural networks used in seismology are single-station models trained using single-station (pair) data. However, most seismic methods require knowledge of the spatial positions between multiple stations. Multiple stations contain rich interrelationships and spatial information that cannot be exploited by single-station models. We proposed a multistation neural network structure Transformer Graph Convolutional Network (TGCN) that utilizes temporal attention and spatial attention to capture spatiotemporal information for predicting relative travel-time shifts. Before that, we introduced a method that treats station pairs as nodes and constructs a graph with multiple station pairs. We collected original ambient noise waveforms from 2017 to 2019 in the Alaska region and 2010 to 2014 in the southern California region to obtain relative travel-time shift sequences of station pairs for model training and testing. To showcase the improvement of spatial information to the model, we compared TGCN with two other baseline single-station models—temporal convolutional network and long short-term memory. Our proposed method predicted travel-time values more accurately than the two baseline models, and it also exhibited slower decay in performance when predicting over larger intervals. We also found that the number of station pairs has an impact on the model. When there are a sufficient number of station pairs, the model can effectively utilize the rich spatial information and achieve higher accuracy. Our approach, which incorporates spatiotemporal information, provides outputs that are more efficient and accurate compared with the traditional single-station (pair) method that only considers temporal information, suggesting that spatial information does enhance the performance of the model.

Sustainable group tourist trip planning: An adaptive large neighborhood search algorithm

The tourism industry is a key driver of economic growth and contributes to the achievement of sustainability goals. This paper presents a multi-objective group tourist planning problem that considers economic, environmental, and social dimensions simultaneously. The proposed model minimizes total cost and environmental impacts while maximizing the total collected prizes from tourists' interests. We introduce the lost profit opportunity for the cost of tours from an economic perspective for the first time. From an environmental perspective, the model minimizes both carbon emissions for transportation and the waste produced by tourists. Social satisfaction is addressed by considering tourists' preferences for visiting tourist sites and their interests in participating in group tours, maximizing total collected prizes. Uncertainties in travel time and prize values are addressed by using a fuzzy programming approach. A multi-objective adaptive large neighborhood search (ALNS) algorithm is developed to solve the proposed multi-objective group tourist planning problem, offering various removal, insertion, and local search heuristic procedures. Extensive analyses and computations are conducted to demonstrate the performance of the proposed multi-objective optimization model and the ALNS metaheuristic algorithm in solving large-scale instances. The results demonstrate the effectiveness of our approach in aiding tourism managers to make informed decisions that balance economic, environmental, and social objectives.

Health indicators, mode choice, and trip time-cost trade-offs: Evidence from out-patient trips to health care facilities

This paper analyzes whether health indicators affect the values that out-patient trip-makers place upon their travel times. Data for the analysis is a biennial longitudinal sample from 2011 to 2015, comprising individuals in China 45 years of age and older. For nineteen health factors that characterize health behaviors, disease events, physical mobility, and mental health, the analysis estimates baseline mode-choice models and separate models for respondents with and without a negative health factor present. The study documents increasing trends in travel time sensitivities and values of travel time, and finds that health status affects travel time sensitivities and values of travel time. Problems with physical mobility lower out-patient trip makers' values of travel time, whereas health factors related to non-physical mobility conditions tend to raise values of travel time. The study includes robustness tests and a discussion of endogeneity. The study's findings have implications for older population mode choices, transport and health infrastructure projects focused on providing more access, and for cost-benefit analyses whose benefits include value of travel time savings.

Comparison of passenger wait and travel time perceptions at bus stops

Passenger's perceived time is important in improving the public transport system. Since it is only possible to reach these values with a survey, it is very difficult to obtain them. Therefore, studies on this subject are very few and valuable. In the study, passengers' perceived waiting time and travel time values were determined at bus stop. In order to obtain these values, a survey was conducted with the passengers. The linear relationship between waiting time and travel time data was examined and a model was created. The data obtained with the basic regression model, which is the basis of machine learning, is divided into 20% test and 80% training data. The estimation was made with 85% success with the tested data.

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.

Research on Heterogeneous Traveler Travel Mode Choices with Differences under a Mixed Traffic Environment.

Autonomous vehicles (AVs) have been made possible by advances in sensing and computing technologies. However, the high cost of AVs makes privatization take longer. Therefore, companies with autonomous vehicles can develop shared autonomous vehicle (SAV) projects. AVs with a high level of automation require high upgrade and use costs. In order to meet the needs of more customers and reduce the investment cost of the company, SAVs with different levels of automation may coexist for a long time. Faced with multiple travel modes (autonomous cars with different levels of automation, private cars, and buses), travelers' travel mode choices are worth studying. To further differentiate the types of travelers, this paper defines high-income travelers and low-income travelers. The difference between these two types of travelers is whether they have a private car. The differences in time value and willingness to pay of the two types of travelers are considered. Based on the above considerations, this paper establishes a multi-modal selection model with the goal of maximizing the total utility of all travelers and uses the imperial competition algorithm to solve it. The results show that low-income travelers are more likely to choose buses and autonomous vehicles with lower levels of automation, while high-income travelers tend to choose higher levels of automation due to their high value of travel time.

Economic Evaluation of Traffic Congestion & Design of Traffic Signal with Simulation at Ottapalam

Abstract: Traffic congestion is the most outstanding performance of the negative externalities of urban road traffic, which is characterized by the negative effects of time delay, energy waste, air pollution and many other impacts. It affects people’s daily life and could cause stress other than time and monetary loss. Traffic congestion causes negative impacts to transport sector and cause a massive increase in the transportation cost. Time is a precious commodity that we all have an equal amount of, and it is something that should not be wasted. A lot of our time is being wasted due to traffic congestion. Therefore, the study of impact of delay at important and busy road stretches in monetary terms is important. This study evaluates the traffic congestion cost at Ottapalam city 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 results obtained indicated that there was a considerable monetary loss due to traffic delay in the form of opportunity costs. So, there was a need to reduce this monetary loss by bringing suitable measures. The traffic condition of the selected road is found out using simulation by PTV Vissim software after implementing a traffic signal at a busy intersection between the road stretch. The reduction in traffic congestion shows the rightful implementation of the traffic signal.

Blood Donors' Preferences Toward Incentives for Donation in China

Blood donation is critical for health care systems, but donor retention remains challenging. Understanding donors' preferences can inform incentive design and improve retention rates. To identify donor preferences for incentive attributes and their relative importance in promoting blood donation among Chinese donors in Shandong. This survey study fielded a discrete choice experiment (DCE) with a dual response design among blood donors, analyzing the responses under forced and unforced choice settings. The study took place from January 1 to April 30, 2022, in 3 cities (Yantai, Jinan, and Heze) representing diverse socioeconomic strata in Shandong, China. Eligible participants were blood donors aged 18 to 60 years who had donated within the preceding 12 months. Participants were recruited using convenience sampling. Data were analyzed from May to June 2022. Respondents were presented with different blood donation incentive profiles, varying in health examination, blood recipient, honor recognition, travel time, and gift value. Respondent preferences for nonmonetary incentive attributes, attribute relative importance, willingness-to-discard values for attribute improvement, and estimated uptake of new incentive profiles. A total of 650 donors were invited, of which 477 were included for analysis. The respondents were predominately male (308 respondents [64.6%]), aged 18 to 34 years (291 respondents [61.0%]), and had undergraduate degrees or higher (286 respondents [59.9%]). Among the 477 valid respondents, the mean (SD) age was 31.9 (11.2) years. Respondents preferred comprehensive health examination, family members as recipients, central government recognition, 30-minute travel time, and a gift valued at 60 Renminbi (RMB). No significant differences were found between the model results of forced and unforced choice setting. Blood recipient was the most important attribute, followed by health examination and gifts, and then honor and travel time. Respondents were willing to discard RMB 32 (95% CI, 18-46) for an improved health examination and RMB 69 (95% CI, 47-92) for changing the recipient from themselves to family members. Scenario analysis estimated 80.3% (SE, 0.024) of donors would endorse the new incentive profile if the recipient was changed from themselves to family members. In this survey study, blood recipient, health examination, and gift value were perceived more important as nonmonetary incentives than travel time and honor recognition. Tailoring incentives according to these preferences may improve donor retention. Further research could help refine and optimize incentive schemes for blood donation promotion.

Analysis of methods for accounting cargo vehicles in traffic flow of a regulated crossroad

Introduction. The formation of a ‘cargo frame’ around large cities leads to a change in the composition of the traffic flow on bypass roads, and an increase in the total traffic flow of trucks of various categories. In addition, the active development of suburban areas with residential complexes contributes to an increase in traffic. The totality of the reflected phenomena leads to the emergence of a certain kind of transport problems, primarily associated with a decrease in throughput. Quite often, such problems are observed at regulated intersections, which requires a prompt change in the control mode; for this, the necessary measure is the mandatory consideration of the technical and dynamic parameters of trucks, which are not fully taken into account in the existing reduction factors. In order to establish the degree of influence of these parameters on the change in the main characteristics of the traffic flow, such as the average travel time and average speed, this study was carried out.Methods and materials. When performing the study, the methods of natural observation, statistical analysis and modelling were applied. The necessary materials for the study were devices for automatic collection of traffic flow characteristics, such as video cameras and traffic detectors, Any Logic version 8.0 for modelling, and a package of descriptive statistics in MS Excel.Results. In the course of the study and the experiment, a difference in the main characteristics of the traffic flow when using standard reduction factors and without using them, taking into account the dynamic and technical parameters of trucks was established. At the object of the study, the difference in the value of the average travel time () for various methods of accounting for trucks, observed in the range [-51.5%; 16.8%] and average speed () in the range [20%; 34%] was determined. As a result of mathematical research, functional relationships between the average speed and traffic intensity are determined, taking into account the presence of trucks of various categories, their technical and dynamic parameters. The ways of further research are determined.

Drivers’ attention economy and adoption to autonomous vehicle

Limits in attention restrict drivers from performing many desired non-driving activities while manoeuvring their vehicles. This constraint facilitates the use of transport services with autonomous vehicles (AVs). In this study, we interviewed 763 car drivers about their driving behaviour, their driving-based multitasking habit, and their driving-based smart device addiction. We constructed an integrated choice model with latent variable (ICLV) and identified the latent effects of two variables on both the preference for AV-based services and the value of travel time (VTT) at an individual level. Our results suggest that the multitasking habit discourages a driver from switching to AV-based services, while the driving-based device addiction encourages drivers to do so. This is expected, since drivers having a stronger driving-based multitasking habit are likely to be more capable of carrying out non-driving activities while manoeuvring their vehicle. As a result, skilful drivers may be reluctant to adopt an AV service. In contrast, those who demonstrate a higher level of driving-based addiction to their smart device are more likely to embrace AV services, since the addiction implies a desire to focus their attention on it instead of on driving. We found a stronger multitasking habit to reduce VTT in both AV and public transport services. In contrast, addiction to smart device increases generic cost, increasing VTT. We further investigated factors affecting how these two latent variables form and we identify young, male drivers (under 40 years old) that drive very frequently (> 3 times a day) should be the ideal advertising targets should AV-based services are rolled out.

Valuation of travel time

This study examines the valuation and use of the transit time leg of a tourism journey. The transit leg of a trip may be perceived as the core component of the trip, an intrinsic part that is valued more or less the same as being at the destination or a chore that must be completed in order to get to the destination. The valuation of transit time is subtle and nuanced and reflects whether the person undertaking the trip has a goal or process orientation. This study develops a conceptual model showing the continuum between process and goal oriented tourists.

The economic cost of a 130 kph speed limit in Germany

Germany remains the only large country in the world without a general speed limit on highways. One of the main arguments for this policy is that lower speeds represent a travel time cost that is not outweighed by benefits, such as a reduction in greenhouse gas emissions. As transport decision making in the European Union is based on cost-benefit analysis (CBA), this paper compares the value of travel time, fuel consumption, infrastructure, crashes, carbon dioxide (CO2), and air pollution. Results suggests that, at the low end of the estimate, a 130 kph speed limit will generate welfare gains in the order of 950 million Euro per year. The ‘no speed limit’ policy consequently represents a subsidy forwarded to fast drivers. The paper also discusses the views of the public vis-à-vis automobile lobbies, and the relevance of Germany's climate change mitigation law, mandating that transport systems be decarbonized. The CBA suggests that a 130 kph speed limit is a policy field where environmental concerns, positive economic effects, and public opinion can be aligned.

Correction to: A minute of your time: The impact of survey recruitment method and interview location on the value of travel time

Correction to: A minute of your time: The impact of survey recruitment method and interview location on the value of travel time

Automated Vehicles: Changes in expert opinions over time

Innovations in transportation can contribute to reaching relevant societal objectives (e.g. reduce emissions, congestion levels and/or risks). To determine which innovations to stimulate (and which not) it is important for policy makers to assess their effects on beforehand. Expert judgement is an often-used method for this purpose. Although expert opinions can provide useful input for decision-making, these judgement are potentially subject to change, for example, due to new information becoming available or because the (academic) discussion about the innovation in question takes a new turn. Studies that explicitly study stability and change in expert judgements are rare, but highly relevant to determine the reliability of experts' assessments. To address this gap, this study assesses experts’ judgments at multiple points in time focusing on the effects of a transport innovation with potentially widespread societal implications, namely Automated Vehicles (AVs). To this end a survey was administrated to a group of experts involved in the WISE-ACT project. The results indicate that expert opinions towards this innovation are generally favorable; AVs are believed to reduce congestion delays, greenhouse gas emissions and traffic accidents. Although the consulted experts are generally quite stable in their opinions, they are becoming less optimistic about these positive effects over time. A Q-factor analysis additionally reveals two clusters of experts, one with a positive and one a negative outlook on AVs. The latter group beliefs that AVs will lower the value of travel time, thereby increasing travel demand, and, in turn, emission and congestion levels. Overall, the changing and diverging opinions among experts indicate that the transition to AVs is not necessarily regarded as a desirable one.

Factors influencing the perceived value of travel time in European urban areas

This research aims at expanding the scope of travel satisfaction by incorporating subjective elements in the evaluation of worthwhileness of travel time proposed by the H2020 MoTiV project, using a European-wide mobility dataset collected in 2019. Trip characteristics, mood, socio-demographic characteristics, experience factors, travel activities and weather were analysed to explore their influence on travellers’ perception of worthwhileness of travel time. The analysis was performed separately for five different transport mode categories using Structural Equation Models. The empirical analysis of this research indicates the high significance of enjoyment in terms of making a trip worthwhile for all transport modes. The results also revealed mode-specific experience factors that play an important role in shaping travel experience which could be addressed to improve the quality of travel time. It is expected that these results can support a shift towards a more human-centric approach in urban mobility plans as well as pave the way for more inclusive transport policies.

Estimating the Value of Airport Access Time in Developing Countries with a Case Study of Nanjing, China

The valuation of time is one of the most important public policy issues in project cost-benefit analysis. This paper estimates the value of airport access time and time variability in developing countries with a case study of Nanjing, China. An international meta-analysis is being used to identify the factors that may affect heterogeneity in the value of travel time. Regression models are then established for the prediction of the value of travel time. The results provide some new insights into the impacts of survey region, traffic mode, and trip purpose on the value of travel time. Considering the significant influencing factors that were obtained, stated preference surveys are designed and used to collect data on preferred arrival time and decision choice under various hypothetical situations. A multivariate regression model is used with the data to explore the significant factors that influence the travelers’ preferred arrival time. Mixed logit models are developed to estimate the value of airport access time, value of schedule delay early, and value of schedule delay late by incorporating the effects of travel delay variability on users’ scheduling costs. The tax system is being used to illustrate the contribution of different income groups to social funds, which also calculates the social value of airport access time, social value of schedule delay early, and social value of schedule delay late. The results identify the significant factors that may affect the valuation of airport access time and provide reasonable estimates for these values. The findings also bring new enlightenment on the effects of the variation of airport access time.

Determining the Optimal Route of Electric Vehicle Using a Hybrid Algorithm Based on Fuzzy Dynamic Programming

In this work, a new method for identifying the optimal route of electric vehicles based on a hybrid model is offered. A combination of analytical hierarchical method and fuzzy dynamic programming was used to create the hybrid model. The model designed in this manner allows each user of an electric vehicle to decide the ideal route based on their needs. The case study was created using actual data from the city of Nis. The scenarios for two electric vehicle users for the schedule of charging stations and routes for the city of Nis with predefined values of travel time, charging time, and the attractiveness of the location are analyzed. Based on the acquired findings, all of the suggested model's advantages can be seen, which are principally reflected in bringing electric vehicle users to the forefront, i.e., harmonizing electric vehicle users' needs on the one hand and travel length and charging time at charging stations on the other.

Correlated random parameters model with heterogeneity in means for analysis of factors affecting the perceived value of road accidents and travel time

Road safety funding and management have become important issues in improving the quality of life of road users and there is evidence of a difference in driving behavior and the factor of road use between urban and rural areas, which is, in turn, reflected in different road safety evaluations. The purpose of this study is to assess the financial losses caused by road accidents on Thailand’s highways and the related factors empirically, deploying the willingness-to-pay (WTP) approach. Data were obtained from 640 urban and 960 rural car drivers using a stated choice questionnaire and face-to-face interviews. This study used Correlated Random Parameters Binary Logit with Heterogeneity in Means (CRPBLHM) approach to analyze factors affecting WTP. According to the results, the value of a statistical life and injury for urban drivers was 1.63 times higher than that for rural drivers, and the value of travel time reduction per hour for urban drivers is ∼1.14 times higher than that found for rural drivers. Furthermore, the results of the CRPBLHM model reported that there are significant differences between urban and rural drivers’ safety intentions and WTP. In the urban model, it was found that driving behavior (ticket, seatbelts, and speed) and weekday trips significantly increase the WTP, whereas household size and gender are unobserved characteristics among drivers. The rural model revealed that a driver’s education (bachelor’s and master’s degrees held), and a necessary trip were significantly associated with drivers’ valuation of safety; moreover, it was found that household size, sole earner status, own accident, in possession of a doctoral degree, and being young were significant in acting as unobserved characteristics. The results demonstrated differences in the value of road safety and unobserved heterogeneity among drivers, which influence risk perception and valuation with reference to the area context. Relevant agencies can use the results as a guideline for budget allocation and practical policy-related road safety management.