Travel Time Variability Research Articles

Why Do Students Choose Buses over Private Motorcycles and Motorcycle-Based Ride-Sourcing? A Hybrid Choice Approach

Improving public transport (PT) services is critical to maintaining resilient and sustainable transportation. While the investment in PT (i.e., buses) has not been able to shift private vehicle users, especially motorcyclists, to buses, the emergence of motorcycle-based ride-sourcing (MBRS) has become a popular travel mode that threatens the existing bus demand. Due to this, this study aimed to investigate the factors affecting the choice of buses over private motorcycles and MBRS among high school and university students in Yogyakarta, Indonesia, through the hybrid choice model. The explored variables include sociodemographic characteristics, travel time, travel cost, and latent variables of travel mode perceptions. By involving 527 respondents, the analysis showed that travel time, travel cost, and sociodemographic factors, including education level, income, and motorcycle ownership, significantly impacted travel mode choices. The findings also revealed that convenience causes buses to be the chosen transport means among students, while lifestyle generates students to choose MBRS services. Based on findings, this study proposed some policies to increase bus demand, especially related to the identified convenience factors and travel time.

An integrative approach to neighbourhood sustainability assessments using publicly available traffic data

Travel time data and variability of travel times can be used to quantify the network performance as an important aspect of sustainable development in modern cities. We introduce a data collection and analysis framework to be employed as an integral part of a neighbourhood sustainability assessment (NSA). The proposed framework performs a targeted selection and evaluation of indicators based on publicly available traffic data, specifically addressing short-term observations of travel times and traffic counts. Entailing a hybrid and modular approach, we demonstrate the applicability of travel time reliability measures by combining different datasets, various variability estimators, and visualisation methods, to advance the interpretation strength of the assessment. Implemented are regression methods based on cosinor models, which allow us to analyse the rhythmic behaviour of travel time trends. These models can be applied to assess the differential rhythmicity among different factors, like route, weather conditions and type of day (i.e., weekend or workday). Further, the same datasets are used to evaluate two reliability metrics, i.e., a travel time index and a planning time index. We demonstrate the application of the suggested framework on several strategic routes associated with three testbed neighbourhoods in Ljubljana, Slovenia. The results show that average travel times and their variations are affected by route, day of the week, and weather conditions, and correlate with vehicles count data. Analyses of rhythmicity give straightforward insights into travel time trends, appropriate for comparative analysis and thus well-suited for use in NSA and monitoring practices. To the best of our knowledge, this work presents the first study quantifying reliability indicators intended for integration with NSA standards.

An augmented Lagrangian relaxation method for the mean-standard deviation based vehicle routing problem

With the ever-increasing trip demand and limited transportation resource, traffic congestions remain a tricky problem. Under traffic congestions, travel times are unstable and variable in a real-world network. When we conduct many time-sensitive vehicle routing optimizations such as vaccine transportation, it is essential to consider the travel time variability in these optimizations. However, most previous vehicle routing studies neglected the travel time variability and adopted the expected travel times or lengths of links. The historical travel times of multiple days allow us to access and describe the travel time variability. We can easily obtain the travel time means and standard deviations of links from these data. This study applies these two statistical indicators to vehicle routing optimization and concentrates on a mean-standard deviation based vehicle routing model. This model has a nonlinear and concave objective function that dramatically increases the problem complexity. We develop a novel augmented Lagrangian relaxation approach for this problem. A lower bound is constructed for this problem by applying the Lagrangian relaxation and decomposition technique twice. To break the inherent symmetry of this problem, we use the augmented Lagrangian relaxation method with block coordinate descent, linearization, and Lagrangian substitution techniques to achieve high-quality feasible solutions. Thus, the relative gap between the lower and upper bounds can be evaluated and reduced in the solving procedure. We implement the proposed method on the datasets modified from the standard benchmark instances. The average relative gaps of instances in sets A and P are 8.8% and 7.6%, respectively.

A conservative expected travel time approach for traffic information dissemination under uncertainty

Travel time is important for both users and planners. Travel time variability widely exists in transportation systems. Positive skew with a long/fat upper tail is a basic characteristic of travel time variability, which poses great challenges to reporting travel time information to the public. There are two main approaches for disseminating travel time variability information: the traditional expected value and the percentile-based value. They are either unreliable when facing highly-skewed travel time variability, difficult to understand, or sensitive to individual risk preference. Motivated by this observation, this paper develops a conservative expected travel time (CET) approach to enhance the information reliability and maintain simplicity, which is formulated as a two-stage problem: (1) reshaping the travel time distribution, and (2) minimizing the expected value of the reshaped distribution. The features of the CET and the relationship between the CET and traditional measures are explored in both theoretical and numerical dimensions.

Joint optimization of timetabling, vehicle scheduling, and ride-matching in a flexible multi-type shuttle bus system

Shuttle bus services play a vital role in public transit systems. However, conventional shuttle bus systems use the same type of buses and fixed departure timetables. They fail to meet the time-varying travel demand and do not consider the diversified characteristics of transit passengers. With the advent of the information era, communication among passengers, buses, and dispatching centers has become much easier. Accordingly, demand-responsive bus dispatching methods are expected to increase the flexibility of operation schemes to improve the performance of shuttle bus systems. To this end, this study proposes a mixed-integer linear programming model to incorporate ride-matching and shuttle bus dispatching in one framework. The bus trip schedules (including the number of used bus trips of different types, departure times, dwelling stops with dwelling times, and recommended travel speeds between adjacent stops of each trip) and passenger-to-vehicle matching schemes are optimized. The commonly used stop-skipping tactic, speed adjustment, and bus holding strategies are introduced to improve the flexibility and operational efficiency of shuttle bus systems. The objective is to minimize the weighted total passenger waiting times, passenger in-vehicle times, and operating costs. A modified Lagrangian relaxation algorithm is designed to improve computational efficiency for large-scale problems. A rolling horizon scheme is designed to implement the algorithm dynamically. It updates the dispatching scheme horizon by horizon according to newly collected requests, which can also improve the algorithm performance by reducing the problem scale. Numerical results show that the proposed flexible multi-type shuttle bus system outperforms a multi-type bus timetabling algorithm in all the considered cases, and it outperforms an on-demand ride-sharing algorithm in 80% of the cases.

A New Probe Into the Innermost Inner Core Anisotropy via the Global Coda‐Correlation Wavefield

AbstractInvestigations of the Earth's inner core (IC) using seismic body waves are limited by their volumetric sampling due to uneven global distribution of large earthquakes and receivers. The sparse coverage of the IC leads to uncertainties in its anisotropy, the directional dependence of seismic velocity. Yet, detailed constraints on anisotropy, such as its magnitude, and spatial distribution, are required to understand the crystallographic structure of IC's iron and its solidification and deformation processes. Here, we present a new method to investigate the IC's anisotropic properties based on Earth's coda‐correlation wavefield constructed from the late coda of large earthquakes. We perform a comprehensive travel time analysis of I2*, an IC‐sensitive correlation feature identified as a counterpart of the direct seismic wavefield's PKIKPPKIKP waves, yet fundamentally different. Namely, I2* is a mathematical manifestation of similarity among specific seismic phases with the same slowness detected in global correlograms in the short inter‐receiver distance range. Our new spatial sampling of the IC overcomes the shortage of direct seismic wavefield paths sensitive to the IC's central volume, also known as the innermost IC (IMIC). The observed I2*’s travel time variations relative to Earth's rotation axis (ERA) support a model of cylindrical anisotropy with 3.3% strength and a zonal pattern of slow axis oriented 55° from ERA. We thus find compelling evidence for a deep IC structure with distinct anisotropy, although we cannot resolve the depth at which the change occurs. This finding reinforces previous inference on the IMIC, with implications for Earth's evolution.

An Investigation of Travel Time Pattern of T224 RapidKL Bus Using AVL Data

Transit dependability is a measure of the quality of service provided by public transportation networks. The trip time distribution may represent the nature and pattern of travel time variability, and it is required in the reliability analysis. Predicting transit bus travel times along routes is essential in bus design and operation, particularly in metropolitan regions. Bus riders are more likely to trust a transportation system if journey times can be accurately anticipated within a given margin of error. Many studies have been conducted to better understand travel time distribution, however there are very few studies on heterogeneous traffic in emerging countries such as Malaysia. In this work, the Taguchi T- technique and linear regression scales were used to evaluate the journey time distribution of public bus route T224 utilising AVL data. To increase the accuracy of applications, a prediction mechanism should be developed. Finally, a formulation was constructed to test the influence of side roads on prediction accuracy, and it was discovered that the additional need in terms of location-based data had no discernible effect on prediction accuracy. This clearly proved that the suggested method based on vehicle tracking data is adequate for the contemplated use of bus trip time prediction.

A Person-Based Adaptive Traffic Signal Control Method with Cooperative Transit Signal Priority

Real-time traffic signal control has long been a critical way to improve traffic congestion. Transit Signal Priority (TSP) is seen as a cost-effective way to reduce travel time variability. Most of the previous studies develop real-time signal control systems on a vehicle basis, which is unable to efficiently provide preferential treatment on transit vehicles. Person-based signal control systems, which transform traffic delay computation units from vehicle to passenger, have been proposed to try to address this limitation. However, their models, optimizing signal plan cycle-by-cycle, cannot rapidly respond to traffic variations. This study proposes a Person-based Adaptive traffic signal control method with Cooperative Transit signal priority (PACT). In PACT, not only do Road-Side Units (RSUs) perform signal optimization, but also On-Board Units (OBUs) provide in-vehicle speed advisory to reduce delays. The interaction between RSU and OBU is conducted second-by-second, which has high adaptability to traffic variations. Experiments are performed based on real traffic data via traffic simulation platform SUMO. The results indicate that PACT can efficiently reduce delays of both bus passengers and auto passengers at a signalized intersection. Compared to preoptimized signal plans, the results show that each passenger on transit vehicles experiences 33%–70% decreases in delays, and each auto passenger experiences 3%–29% decreases in delays. PACT can reduce 80%–98% in delays when the occupancy weight factor is relatively large, showing the potential of extending PACT on performing signal preemption.

Intra-annual relationship between precipitation and forest disturbance in the African rainforest

Analysis of forest disturbance patterns in relation to precipitation seasonality is important for understanding African tropical forest dynamics under changing climate conditions and different levels of human activities. Newly available radar-based forest disturbance information now enables an investigation of the intra-annual relationship between precipitation and forest disturbance in a spatially and temporally explicit manner, especially in the tropics, where frequent cloud cover hinders the use of optical-based remote sensing products. In this study, we applied cross-correlation on monthly precipitation and forest disturbance time series for 2019 and 2020 at a 0.5° grid in the African rainforest. We used the magnitude of the correlation and time lag to assess the intra-annual relationship between precipitation and forest disturbance, and introduced accessibility proxies to analyse the spatial variation of the relationship. Results revealed that a significant negative correlation between forest disturbance and precipitation dominates the study region. We found that significant negative correlations appear on average closer to settlements with overall smaller variations in travel time to settlements compared to grid cells with non-significant and significant positive correlation. The magnitude of the negative correlation increases as the travel time to settlements increases, implying that forest disturbances in less accessible areas are more affected by precipitation seasonality and that in particular human-induced disturbance activities are predominantly carried out in the drier months. Few areas showed a significant positive correlation, mainly resulting from natural causes such as flooding. These new insights in the interaction between forest disturbance, precipitation and accessibility provide a step forward in understanding the complex interactions that underlie the complexity of forest loss patterns that we can increasingly capture with Earth Observation approaches. As such, they can support forest conservation and management in coping with climate change induced changes of precipitation patterns in African rainforest countries.

Traveller Recurrence and Inter- versus Intratraveller Speed Variability: Analysis with Bluetooth Data

This paper proposes a linear mixed model of route speed distributions that separates the variability into an intertraveller component, consistent across days and time intervals for each recurrent traveller, and an intratraveller component representing uncertainty. The intratraveller variability corresponds to travel time uncertainty, while the total variability is typically captured by empirical measurements and used in travel time reliability assessments. The intratraveller and the total variability differ if there are systematic differences in speed between different recurrent travellers. The paper also investigates to what degree vehicles traversing a route during the morning or evening peak over multiple days are recurrent travellers. Using data from Bluetooth and Wifi sensors on 26 routes in Stockholm, Sweden, over a three-month period, we find that the traveller recurrence is higher towards the city in the morning peak and out from the city in the afternoon. Model estimation results show that the relative intratraveller variability is also significantly higher in the commute direction (towards the city in the morning and out from the city in the afternoon) and on routes with high congestion levels. The relations revealed in this paper may be used to estimate the relevant intratraveller variance based on the total variance and readily available route attributes. Without this correction, the costs associated with travel time variability may be overestimated.

Pricing strategy of ride-sourcing services under travel time variability

Ride-sourcing services are burgeoning urban mobility services provided by transportation network companies (TNCs), consisting of non-pooling service and ride-pooling service. By receiving online orders and offering efficient door-to-door services, ride-sourcing services are becoming popular alternatives to public transit. Meanwhile, unignorable travel time variability influences passengers’ travel behavior and TNC’s pricing strategy. To investigate the effect of travel time variability, this paper proposes a bi-level framework. The mode choice of passengers, zone choice of searching drivers and the subsequent route choice traffic equilibrium in a transportation network is formulated as the lower level. And the upper level investigates the optimal pricing strategy. In the network formulation, passengers encounter variability of waiting time, detour time and highway travel time. Two pricing strategies are proposed to accommodate the risk of unsuccessful pairing. Pricing strategy 1 charges a fixed price even when a ride-pooling request becomes a non-pooling service due to unsuccessful pairing; whereas pricing strategy 2 increases the price of a ride-pooling request to that of non-pooling service if pairing is unsuccessful. The results show that pricing strategy 1 produces more profit and induces more ride-pooling demand. In addition, under more reliable traffic conditions, the optimal prices for ride-sourcing services are higher and the network travel time decreases. The results illustrate the influence of travel time variability on the ride-sourcing market and provide guidance on the company’s pricing strategy regarding a network under travel time variability.

Technical note: A graphical approach to the analysis of travel times in an automated storage and retrieval system

AbstractOperating time is closely related to the performance of an order‐picking system. Knowing the probability distribution of operating time will greatly facilitate system performance analysis and optimization. When using an automated storage and retrieval system (AS/RS), travels of the storage and retrieval machine can be modeled using the Chebyshev metric. However, because of the dependence among the travel legs, analyzing the variance of travel time can be quite challenging. In this paper, a graphical approach based on contour lines is proposed for deriving the probability density function (PDF) of travel time in an AS/RS. With the PDF, the expected value and variance of travel time can be obtained. To demonstrate the use of the proposed approach in evaluating the performance of an AS/RS under stochastic conditions, we derive the PDF of travel time and apply it in a queueing analysis. Based on the queueing performance measures and an overall arrival rate, the minimum number of operation requests waiting or being served occurs when the arrival rates of storage and retrieval requests are approximately equal. Also, when arrival rates of storage and retrieval requests are equal, the number of operation requests waiting or being served is minimized when the rack is square‐in‐time.

A Random Effects Model for Travel-Time Variability Analysis Using Wi-Fi and Bluetooth Data

AbstractThis study examined variations in travel time through a random-effects model using Wi-Fi and Bluetooth data. Previous studies mainly divided a day into discrete time windows and estimated a...

A novel multi-objective green vehicle routing and scheduling model with stochastic demand, supply, and variable travel times

We propose an optimization approach to the routing and scheduling problem for a heterogeneous transportation network that considers (i) stochastic values of demand and supply at the nodes, (ii) variable travel times between nodes conditioned by the fatigue of drivers, (iii) maximum allowed continuous driving time, and (iv) soft service time windows per customer at the nodes. The problem minimizes energy consumption and maximizes customer satisfaction. The subsequent stochastic multi-objective mixed integer programming model is solved using a hybrid approach based on chance- and epsilon-constraint methods. Given the NP-hard quality of the model, we introduce a hybrid meta-heuristic method based on genetic algorithm (GA) and simulated annealing (SA). This novel technique, named MOGASA, combines the global and local search capacities of both meta-heuristic algorithms, providing an intuitive solution approach that allows to solve problem instances considering large distribution networks with multiple types of vehicles in reasonable CPU time. We illustrate how MOGASA improves upon the hybrid chance- and epsilon-constraint exact solution method, particularly when dealing with large problem instances that cannot be solved by the latter. A medium instance scenario is used to analyze the reaction of the objective functions and the subsequent Pareto frontiers to modifications in the main structural parameters defining the transportation network. Potential applications of our stochastic framework to different types of logistic structures and retail supply chains are highlighted.

A national evaluation of geographic accessibility and provider availability of obesity medicine diplomates in the United States between 2011 and 2019.

Background/Objectives:Obesity is a pressing health concern within the United States (US). Obesity medicine “diplomates” receive specialized training, yet it is unclear if their accessibility and availability adequately serves the need. The purpose of this research was to understand how accessibility has evolved over time and assess the practicality of serving an estimated patient population with the current distribution and quantity of diplomates.Methods:Population-weighted Census tracts in US counties were mapped to the nearest facility on a road network with at least one diplomate who specialized in adult (including geriatric) care between 2011 and 2019. The median travel time for all Census tracts within a county represented the primary geographic access measure. Availability was assessed by estimating the number of diplomates per 100 000 patients with obesity and the number of facilities able to serve assigned patients under three clinical guidelines.Results:Of the 3 371 diplomates certified since 2019, 3 036 were included. The median travel time (weighted for county population) fell from 28.5 minutes [IQR: 13.7, 68.1] in 2011 to 9.95 minutes [IQR: 7.49, 18.1] in 2019. There were distinct intra- and inter-year travel time variations by race, ethnicity, education, median household income, rurality, and Census region (all P < .001). The median number of diplomates per 100 000 with obesity grew from 1 [IQR: 0.39, 1.59] in 2011 to 5 [IQR: 2.74, 11.4] in 2019. In 2019, an estimated 1.7% of facilities could meet the recommended number of visits for all mapped patients with obesity, up from 0% in 2011.Conclusions:Diplomate geographic access and availability has improved over time, yet there is still not a high enough supply to serve the potential patient demand. Future studies should quantify patient-level associations between travel time and health outcomes, including whether the number of available diplomates impacts utilization.

Revealing the spatial variation in vehicle travel time with weather and driver travel frequency impacts: Findings from the Guangdong–Hong Kong–Macao Greater Bay Area, China

&lt;abstract&gt; &lt;p&gt;Vehicle travel time information is an essential location-based services that can be used to assess highway traffic conditions and provide valuable insights for transit agencies and travelers. To reveal the spatial variation in vehicle travel time with multiple factors, a multiple regression model and a geographically weighted regression model are used to investigate the associations between travel time and various factors. This study draws on freeway toll data in combination with local weather station records on Fridays over 12 months (286, 406 travel information data points), and the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, is used as a case study for examining the influence of weather and driver travel frequency on vehicle travel time. The results show that i) travel frequency along an origin-destination (OD) route has a significant effect on travel time, and this effect is approximately 3 to 100 times that of other explanatory variables; ii) rainfall significantly impacts travel time, with an effect that is 1.9 to 8.26 times that of other weather factors; and iii) both weather and driver travel frequency factors display spatial heterogeneity. These findings provide valuable insights for both traffic management and freeway travelers.&lt;/p&gt; &lt;/abstract&gt;

Modeling of Travel Behavior in Budapest: Leisure Travelers

People usually travel with different preferences to reach the location of their leisure activities, where transport mode is a decisive factor. Previous studies focus on developing discrete choice models for people where a trip purpose is considered as an explanatory variable. In this paper, discrete choice modeling is applied to model the behavior of leisure travelers, to understand the impact of several factors on the transport mode choice, such as sociodemographic variables. A sample of 1100 travelers from a household survey in Budapest is used in the analysis. The sample includes the daily activity plans of leisure travelers and their sociodemographic as well as economic characteristics. A Multinomial Logit (MNL) model is applied, where the data are examined across travel time variables and travel characteristics, such as travel time, travel cost, age, gender, income, and car ownership. The developed models are used to predict the probability of using the different transport modes based on the characteristics of the travelers and their trips. The model indicates that time and the cost impact travel negatively. While other variables show varied impacts on the transport mode selection. The output of this study is important for decision-makers to understand the patterns of leisure tips in a city.

How is freight distribution affected by travel time unreliability?

The impact of travel time unreliability on freight transport has been extensively investigated in the last 20 years with the main focus being placed on mode and route choice (and using mostly stated preference data). In contrast, freight distribution has been very rarely examined in the literature. In this paper, we describe a study on how travel time unreliability affects interregional freight distribution using (revealed preference) data from the last national transport survey carried out in Iran (2015). Through this study, conducted using spatial interaction models and linear and geographically-weighted regression approaches, we found that, globally, travel time reliability is approximately as important as average travel time in determining freight distribution flows, but this importance varies widely across regions. We also found that tardy trip reliability measures describe freight distribution patterns more accurately than statistical range measures (coefficient of variation of travel time).

Uncertainty and Variability Analysis of Agent-Based Transport Models

This paper presents an analysis of the output variability of agent-based transport models. We simulated a MATSim model of the city of Hanover multiple times with identical input and evaluated the resulting travel times on different level of aggregation. On a global level, we observed minor variations of travel times. However, the results show an increased variation when examining the output on the level of districts or for individual agents. A recommendation for estimating the required number of simulation runs for a stable output of travel time for the purposed aggregation level is derived from our case study.

Operational evaluation of the bus rapid transit system: Case study of Dar es Salaam city

This study evaluated the effectiveness of the first Bus Rapid Transit (BRT) system in Dar es Salaam. A Bayesian Hierarchical modeling approach and Bayesian hypothesis test were used to compare the operational performance of BRT against the non-BRT buses and private automobiles. The study also examined factors affecting the travel times of the three modes of transportation. The study corridor, which is 10.2 kilometers long, is an arterial segment along Morogoro Road. The predicted travel time for BRT was lowest at the 95% Bayesian credible interval, with a mean value of 16 minutes. Non-BRT bus trips had the highest travel time of 28 minutes, followed by personal automobile trips with an average travel time of 18 minutes, indicating that BRT buses improve travel time along the study corridor. Factors such as delay, time of day, dwelling time, number of stops, and trip direction significantly influenced travel time variability along the study routes.