Travel Time Distribution Research Articles

Modelling electrical conductivity variation using a travel time distribution approach in the Duck River catchment, Australia

AbstractSolute dynamics depend strongly on hydrologic flow paths and transit times within catchments. In this paper, we use a travel time tracking method to simulate stream salinity (as measured by electrical conductivity) in the Duck River catchment, NW Tasmania, Australia. The study couples storage selection function transit time modelling with two alternate approaches to model electrical conductivity (EC). The first approach assumes the catchment has a cyclic salt balance (i.e., rainfall source, stream flow sink) that is in dynamic equilibrium and evapoconcentration of salt is the only process changing concentration. The second approach assumes that the salinity of water in catchment storages is a function of water age in those stores, without explicitly simulating salt mass balance processes. The paper compares these alternate approaches in terms of EC simulation performance, simulated stream water age distributions, and simulated storage age distributions. A split sample calibration‐validation analysis was conducted using the 2008 and 2009 water years. Both EC simulation approaches reproduced stream EC variations very well under both calibration and validation. The simulations using the age‐related EC simulation approach produced less biased results and, consequently, higher model coefficient of efficiency for validation periods. This approach also produced more consistent model parameter estimates between periods. There were systematic differences in the resultant age distributions between models, particularly for the solute balance‐based simulations where parameters (catchment storage size) changed more between the two calibration periods. The effect of time varying versus static storage selection functions were compared, with clear evidence that time varying storage selection functions with parameters linked to catchment conditions (flow) are essential for adequate simulation of EC dynamics during flow events.

Optimization of multi-type sensor locations for simultaneous estimation of origin-destination demands and link travel times with covariance effects

This paper investigates the multi-type traffic sensor location problem for simultaneous estimation of origin-destination (OD) demands and link travel times while also considering the two sources of spatial covariance effects on a road network with uncertainties. The first source is the statistical correlation of the vehicular traffic demands for different OD pairs in a typical hourly period (e.g., morning peak hour) on a daily scale, as the travel demand patterns vary daily, weekly, and seasonally over the year. The second source is the stochastic nature of the link travel times on different road links during the peak hour period and their correlation with adjacent links in congested conditions. By considering these aspects, a novel model is formulated to optimize the number and locations of multi-type traffic sensors for simultaneous estimation of stochastic OD demands and link travel times during the same peak-hour period over a year. This estimation is supported by the data recorded by multi-type traffic sensors such as point sensors and automatic vehicle identification sensors, which yield the link speed/flow and path travel time information, respectively. Based on the integrated observations from multi-type traffic sensors, a novel Kullback–Leibler divergence-based model is developed to accommodate different probability distributions of OD demands and link travel times in different traffic conditions. Moreover, an improved firefly algorithm is developed to solve the multi-type sensor location problem. Specifically, the search strategy of this algorithm is enhanced by considering the mean and covariance of the OD demands and link travel times. Numerical examples of synthetic and real-world road networks are used to illustrate the applications and merits of the developed multi-type sensor location model for simultaneously estimating the OD demands and link travel times while also considering covariance effects.

Stochastic optimization models for a home service routing and appointment scheduling problem with random travel and service times

We address a routing and appointment scheduling problem with uncertain service and travel times arising from home service practice. Specifically, given a set of customers within a service region that an operator needs to serve, we seek to find the operator’s route and time schedule. The quality of routing and scheduling decisions is a function of the total operational cost, consisting of customers’ waiting time, and the operator’s travel time, idle time and overtime. We propose and rigorously analyze a stochastic programming model and two distributionally robust optimization (DRO) models to solve the problem, assuming known and unknown service and travel time distributions, respectively. We consider two popular types of ambiguity sets for the DRO models: mean-support and 1-Wasserstein ambiguity sets. We derive equivalent mixed-integer linear programming (MILP) reformulations of both DRO models that can be implemented and efficiently solved using off-the-shelf optimization software, thereby enabling practitioners to use these models. In an extensive numerical experiment, we investigate the proposed models’ computational and operational performance and derive insights into the problem.

Estimation and Reliability Research of Post-Earthquake Traffic Travel Time Distribution Based on Floating Car Data

To carry out the estimation and reliability research of post-earthquake traffic travel time, which has the great influence for efficient allocation of relief materials. By analyzing the relationship among floating vehicle trajectory, target path and road network path, the intermediate parameters of converting floating vehicle trajectory data into target path travel time were defined and improved. In addition, the road damage identification method relying on lane detection is applied for evaluating the damage of road after the earthquake through the image information. Then, Bayesian average adaptive kernel density estimation method was used to estimate the distribution of post-earthquake road travel time, and a new formula for calculating the reliability of road travel time after earthquake was proposed. According to the example simulation and analysis, the proposed post-earthquake road travel time distribution estimation and its reliability are verified. The results show that when the threshold value is determined, the travel time of the path before the earthquake is the most dependable, and with the increase in the earthquake damage index, the travel time of this road section becomes increasingly unreliable. However, after the earthquake, the peak probability density of road travel time distribution weakens, and the overall probability shifts to the direction of long time.

Estimation and simulation for route choice proportion in multi-period urban rail transit control systems

As we all known, estimating the proportion of passenger route choice is of great significance in almost every aspect of urban rail transit control system, including passenger allocation, fare clearing and flow control strategies. Existing researches only pay attention to the route choice through travel time, but usually ignore the influence in different periods of the day. Therefore, this paper proposes a novel estimation method for the proportion of passenger route choice in different periods. Firstly, by introducing the normalized value of passenger flow and the standard coefficient of peak passenger flow, the train operation time is divided into peak and flat periods. Secondly, the travel time distribution of each route can be obtained by estimating the expected value and standard deviation of passenger travel time in each different period. The Naïve Bayes algorithm is further employed to realize the identification of the proportion of passenger route choices. Finally, this proposed algorithm is applied to Hangzhou Metro. The result shows that by using the segmented estimation, the error can be reduced by more than 60% compared with the whole-day experiment, which indicates the superiority of the method.

CTD: Cascaded Temporal Difference Learning for the Mean-Standard Deviation Shortest Path Problem

This paper investigates the reliable shortest path (RSP) planning problem from the reinforcement learning perspective. Different from canonical path planning methods, which require at least the first- order statistic (mean) and second-order statistic (variance) information of travel time distribution, we target at the RSP planning problem without the assumption of knowing any travel time distribution characteristic beforehand, and propose a cascaded temporal difference learning (CTD) method, which simultaneously estimates the mean and variance of the executing path and thereby gradually makes improvements through the generalized policy iteration (GPI) scheme, as the ego vehicle interacts with the environment. Extensive simulation results demonstrate the applicability of the proposed method for RSP learning in various transportation networks.

A Decentralized and Coordinated Routing Algorithm for Connected and Autonomous Vehicles

Aiming for better mobility and more efficient utilization of transportation networks, emergent connected and autonomous vehicle (CAV) technologies, and the resulting communication capabilities can produce more coordinated and efficient routing behavior. Current routing strategies either rely on a centralized control system which can fail in scaling, or employ decentralized schemes that yield sub-optimal coordination and accordingly poor system performance. This paper presents a Decentralized Collaborative Time-dependent Shortest Path Algorithm ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dec-CTDSP</i> ) with which the CAVs optimize their routes according to the communicated mobility messages from the other CAVs within their connected cluster. These messages are assumed to carry information regarding the vehicles’ location, speed, and preferred path to their destination. We analyzed the impacts of this optimization scheme under various levels of CAV market penetration and communication radius. The results of this study reveal (1) Up to 40% improvement in mean system travel time and speed; (2) Up to 45% increase in travel time and speed prediction reliability; (3) A strong correlation between mean system travel time and network usage distribution; and (4) significant improvements in network utilization as a result of Dec-CTDSP. The performance of Dec-CTDSP, in terms of runtime, convergence, and mobility improvements, is further benchmarked against other random and Dijkstra-based algorithms. The findings of this work will help steer further research on the implementation of coordinated and decentralized multiagent routing optimization in the context of connected and autonomous vehicles.

How Random Incidents Affect Travel-Time Distributions

We present a novel analytical model to approximate the travel-time distribution of vehicles traversing a freeway corridor that experiences random quality of service degradations due to non-recurrent incidents. The proposed model derives the generating function of travel times in closed-form using clearance time, incident frequency and severity, and other ordinary traffic characteristics. We validate the model using data from a freeway corridor where weather events and traffic accidents serve as the principal causes of service degradation. The resulting model is equivalent in performance to widely used methodologies while uniquely providing a clear connection on how incidents affect travel time distribution. With this connection, the model readily yields travel time reliability measures for alternative roadway behaviors, providing crucial information for long-term planning.

Vehicle Re-identification for Lane-level Travel Time Estimations on Congested Urban Road Networks Using Video Images

The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lane-level travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method for estimating lane-level travel time distributions using video images from widely deployed surveillance cameras. In the proposed method, a lane-based bipartite graph matching is introduced to obtain optimal matches between upstream and downstream vehicles by considering lane-level traffic conditions and vehicles’ lane changing behaviors and visual features. A lane-based travel time estimation technique is introduced to real-time estimate full spectrum of lane-level distribution parameters, including not only the mean but also the standard deviation and the distribution type. A comprehensive case study is carried out on a congested urban road in Hong Kong. Results of case study show that the proposed method outperforms the state-of-the-art link-based V-ReID method and is capable for providing accurate lane-level travel time distribution information on congested urban roads.

Catchment Dissolved Organic Carbon Transport: A Modeling Approach Combining Water Travel Times and Reactivity Continuum

AbstractQuantifying the transfer of organic carbon from the terrestrial to the riverine ecosystems is of crucial importance to fully appreciate the carbon cycle at the catchment, regional and global scales. In this study, we propose a framework for modeling the flux of dissolved organic carbon (DOC) from hillslopes to stream and river networks which couples a transport model based on travel time distributions with the reactivity continuum (RC) approach to model DOC degradation. We test the model by applying it to the Plynlimon catchments (UK) exploiting both weekly and high‐frequency (7‐hr interval) time‐series. We use information about chloride to get an independent estimate of water travel times using the framework of StorAge Selection functions. Following the RC model, the composition and the degradation of DOC along the flowpaths, and its consequent concentration in the streamflow, is described assuming that DOC is composed by a mixture of compounds that follows a continuous spectrum of reactivity. For the high‐frequency data set, the model is able to reproduce DOC streamflow concentrations and to capture the complex hysteretic relation between DOC concentration and discharge. Weekly data are instead not frequent enough to properly describe DOC dynamics in this catchment. The distribution of the age of the water comprised in the streamflow proves thus a key variable to predict the quantity and the reactivity of the DOC exported from soils, and the effect of hydrologic variability on this process.

Deceptive Decision-Making under Uncertainty

We study the design of autonomous agents that are capable of deceiving outside observers about their intentions while carrying out tasks in stochastic, complex environments. By modeling the agent's behavior as a Markov decision process, we consider a setting where the agent aims to reach one of multiple potential goals while deceiving outside observers about its true goal. We propose a novel approach to model observer predictions based on the principle of maximum entropy and to efficiently generate deceptive strategies via linear programming. The proposed approach enables the agent to exhibit a variety of tunable deceptive behaviors while ensuring the satisfaction of probabilistic constraints on the behavior. We evaluate the performance of the proposed approach via comparative user studies and present a case study on the streets of Manhattan, New York, using real travel time distributions.

Efficacy of Synthetic Sediment Graph Developed using Various Modified Time-Area Methods

Suspended sediment (SS) is an essential indicator for assessing watershed health. However, the temporal variation of SS, called sediment graph (SG) using readily available data, is not always considered, particularly in un-gauged watersheds, which are many in developing countries. Since field measurements of SS are time-consuming and costly, the synthetic SG seems to be a promising alternative. Therefore, it is essential to have reliable SS data for watershed management. This study aimed at simulating SGs through conceptual analysis of soil erosion and sediment yield at the watershed scale. To that end, soil erosion, sediment yield, and sediment routing were modeled using 38 storm events collected during 2011 and 2019 at the Galazchai Watershed in West Azerbaijan Province, Iran. Initially, the Time-Area Method (TAM) was applied, and then two strategies were considered to improve the TAM performance, including RUSLE and sediment delivery ratio (SDR) using gradient ratio and WaTEM/SEDEM methods. Comparing simulated SGs with recorded ones showed that the SDR-based method had the lowest relative error in time to peak and base time, but the peak value had the highest relative error. Results also showed that TAM developed using the spatially distributed travel time method had a better performance than the channel longitudinal profile method. Overall, TAM could not simulate the temporal variation of sediment and needs further research.

Bayesian Mixture Model to Estimate Freeway Travel Time under Low-Frequency Probe Data

This study develops a novel estimation method under low-frequency probe data using the Bayesian approach. Given the challenges in estimating travel time under low-frequency probe data and prior distribution of the parameters in a traditional Bayesian approach, the proposed algorithm adopts a historical data-based data-driven method according to the characteristics of travel time regularity. Due to the variability of travel times during peak periods, this paper adopts a mixture distribution of travel times in the Bayesian approach rather than traditional single distribution. The Gibbs sampling method with a burn-in period is used to generate a series of sampling sequences from an unknown joint posterior distribution for estimating the posterior distribution of the parameters. The proposed algorithm is tested using traffic data collected from the Korean freeway section from Giheung IC to Dongtan IC. Both MAPE and RMSE of the estimation results show that the proposed method has the smallest deviation from the ground truth travel time compared to the simple mean and moving average methods. Moreover, the proposed Bayesian estimation yields the smallest standard deviation of MAPE for all test days. The credible intervals for estimated travel times show that the proposed method provides good accuracy in estimating travel time reliability.

Machine identification method of subway service quality based on smart card data

Abstract A new automatic evaluation method of subway service quality based on metro smart card data is proposed suitable for three different levels: station pair, railway line and subway network, which has merits of overcoming the previous lagging and subjective evaluation in the system of ‘questionnaire survey plus evaluation method’. First, passengers' travel time distribution for different operating periods in station OD pairs are introduced initially for service evaluation purposes and are classified into different groups in order to infer the station's operating characteristics at the different periods. Second, the classification is verified by K-means cluster analysis and K-S tests. Third, the service quality weight indicator is proposed to identify the service quality of the entire metro network from the dual perspectives of passengers and companies. Finally, the feasibility and rationality of the proposed method are verified by Shenzhen metro smart card data as an example. The new automated evaluation method of subway service quality is suitable for online and offline application. Highlights • A new machine identification method based on travel time distribution is proposed for estimating the service quality. • The proposed model employs the K-means clustering method to distinguish passenger's travel time distribution into different modes. • Characteristics of the different modes of passenger travel time distribution reflect different service levels of the urban subway system. • The method provides automatically identification of service levels for urban subway operations.

Geographical accessibility of emergency neonatal care services in Ethiopia: analysis using the 2016 Ethiopian Emergency Obstetric and Neonatal Care Survey

IntroductionAccess to emergency neonatal health services has not been explored widely in the Ethiopian context. Accessibility to health services is a function of the distribution and location of services, including...

How to disseminate reliable waiting time in app-based transportation services considering attractiveness and credibility

Many app-based transportation services provide waiting time information to users before they book the services. Travel time is an important component of waiting time, and positive skew with a long upper tail is a basic characteristic of travel time variability. Waiting time information provision is expected to be reliable and easy to understand. However, the existing information provision forms involving travel time variability are either too optimistic in terms of having a high risk of delay or not easily understood by the users. This paper develops a conservative expected travel time approach with multiple cut points (MCET) for reporting reliable waiting time information to users in app-based transportation services. The MCET is modelled as the expected value of a discretized travel time distribution according to cut points. The numbers and locations of cut points are optimised by seeking a tradeoff between the attractiveness and credibility of the service platform.

High Volume Freeway Travel Time Reliability and the COVID-19 Pandemic

This paper examines the effect of the COVID-19 pandemic on travel time reliability (TTR) performance on two high traffic volume freeway corridors (I-90 and I-405) in the U.S.A. Specifically, the travel time distributions (TTDs) during and before the pandemic are compared. The paper also examines which TTR metrics best capture the effect of the pandemic on reliability performance. There were statistically significant differences, at the 95% confidence level, between TTD in 2020 and corresponding TTDs in 2018 and 2019. Not surprisingly, all measures of central tendency and all measures of dispersions were reduced during the pandemic. Consequently, it was concluded that TTR performance improved during the pandemic regardless of what TTR definition was used. Travel time index (TTI), planning time index (PTI), and the level of travel time reliability (LOTTR) metrics improved during the pandemic, albeit at different rates. In contrast, the buffer index and coefficient of variation increased. In other words, whether an analyst would identify that TTR improved or decreased during the pandemic, and by how much, would be a function of which TTR metric was applied. Not surprisingly, the more congested the roadway section, the greater the impact the pandemic-related interventions had on TTR. It was found that, practically speaking, there are no differences in TTI, PTI, or LOTTR values when TTDs are formulated using a 5 min or 15 min aggregation interval. It was concluded that analysts need to have a deep understanding of the underlying TTD and the various TTR definitions when evaluating changes in freeway systems’ TTR.

A robust survival model for emergency medical services in Delhi, India

We propose a Double Standard Robust Survival Model (DSRSM) for efficient utilization of ambulances to maximize the survivability of high priority calls under uncertain travel times. The DSRSM provides dual coverage using primary and secondary ambulances to maximize the expected survival probability. A mixed-integer linear program equivalent of the quadratic DSRSM is proposed and solved using a combination of Variable Neighbourhood Search metaheuristic and Local Branching technique. The model was applied to the real-world conditions of Delhi, India, one of the largest metropolitan areas in the world. First, a survival probability function was estimated for emergency cases using empirical data from Delhi's public emergency medical services. Next, probability distributions of pre-trip delay, travel time to the demand site, and travel time to the nearest hospital were estimated using field data. The DSRSM utilized survival functions and the travel time distributions to locate ambulances optimally to maximize survivability. Results show that using multiple scenarios to capture varying travel times produces a more realistic performance evaluation for survival outcomes than a single scenario with average travel time. The model produces a single set of ambulance locations each for weekdays and weekends, which is more economically feasible than relocating ambulances across scenarios while providing survival outcomes comparable to the relocation approach.

An Electric Fence-Based Intelligent Scheduling Method for Rebalancing Dockless Bike Sharing Systems

With a new generation of bike sharing services emerging, the development of dockless bike sharing services results in considerable socioeconomic and environmental benefits but also creates new issues, such as inappropriate parking behaviors and bike imbalances. To solve the inappropriate parking problem, electric fences have been introduced to guide users to park bikes in designated zones. Considering the role of electric fences in restricting user parking behaviors, an electric fence-based intelligent scheduling method for rebalancing dockless bike sharing systems is proposed in this paper. As a dynamic method that considers the real-time usage of bike sharing systems, an electric fence adjusts its capacity based on real-time information, which guides users to return bikes to electric fences with greater urgency. Because existing approaches require prespecified models and are unable to consider all the intricacies in the dynamic optimization problem, a model-free intelligent scheduling approach based on deep Q-learning that can adapt to the changing distributions of customer arrivals, available bikes, bike locations, and user travel times is used to solve the problem. Finally, a case study involving Beihang University is employed, which shows that the method performs well in rebalancing the bike sharing system and improving the mean utilization (MU) and customer satisfaction (CS).

Joint Optimization of Ticket Pricing Strategy and Train Stop Plan for High-Speed Railway: A Case Study

In this study, we examined ticket pricing and train stop planning for the high-speed railway (HSR), which integrates two key aspects of railway operation and organization. We considered that passenger demand is sensitive to the generalized travel cost (depending on the ticket price and the travel time) and that the train stop plan can affect the travel time and passenger distribution. Then, a mixed-integer non-linear optimization model was proposed for the joint problem of ticket pricing and train stop planning to maximize HSR’s transport revenue and minimize passengers’ travel time. Based on the high similarity between combinatorial optimization problems and the solid annealing principle, we designed a combined simulated annealing (CSA) algorithm to solve practical problems. The results of a numerical example in the real HSR network showed that the proposed method can improve transport revenue by 5.1% and reduce passengers’ travel time loss by 11.15% without increasing transport capacity.