Travel Time Estimation Method Research Articles

Wildland Firefighter Estimated Ground Evacuation Time Modeling to Support Risk-Informed Decision-Making

Wildland firefighters often work in remote settings with multiple hazards that can cause life-threatening injuries. Prompt access to medical care is key to reducing injury consequences. For the last decade, a spatial model of wildland firefighter estimated ground evacuation time (GET) has been used when developing operational response strategies in the contiguous United States (CONUS). This paper describes our updated and improved GET model and the resulting decision support spatial data representing the estimated time to evacuate to a hospital from anywhere within the CONUS using ground transportation only. The new GET model leverages updated input datasets and has improved off-road travel time estimation methods that incorporate the latest science on how terrain slope influences pedestrian travel rates. It also accounts for a novel set of landscape variables not previously considered, including minor roads and trails, streams, woody debris, cliffs, and an improved handling of shrub cover. When compared to a set of recent safety incidents, the reported evacuation times were correlated with the model predictions. The spatial patterns of GET from the new model are similar to the old product; however, we found that, on average, the new version of GET yields slightly faster evacuation times, but with regional variation in this trend.

Traffic arrival pattern estimation at urban intersection using license plate recognition data

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications.

Evaluating Traffic Operation Conditions during Wildfire Evacuation Using Connected Vehicles Data

With climate change and the resulting rise in temperatures, wildfire risk is increasing all over the world, particularly in the Western United States. Communities in wildland–urban interface (WUI) areas are at the greatest risk of fire. Such fires cause mass evacuations and can result in traffic congestion, endangering the lives of both citizens and first responders. While existing wildfire evacuation research focuses on social science surveys and fire spread modeling, they lack data on traffic operations during such incidents. Additionally, traditional traffic data collection methods are unable to gather large sets of data on historical wildfire events. However, the recent availability of connected vehicle (CV) data containing lane-level precision historical vehicle movement data has enabled researchers to assess traffic operational performance at the region and timeframe of interest. To address this gap, this study utilized a CV dataset to analyze traffic operations during a short-notice evacuation event caused by a wildfire, demonstrating that the CV dataset is an effective tool for accurately assessing traffic delays and overall traffic operation conditions during the selected fire incident. The findings also showed that the selected CV dataset provides high temporal coverage and similar travel time estimates as compared to an alternate method of travel time estimation. The study thus emphasized the importance of utilizing advanced technologies, such as CV data, to develop effective evacuation strategies and improve emergency management.

A Link Real-time Travel Time Estimation on Speed-time Field Traversing Incorporating LSTM Neural Network

A travel time estimation method based on Speed-time field traversal including LSTM neural network was proposed to increase the real-time and accuracy of travel time estimation. The node departure speed of the traditional piecewise truncated quadratic speed trajectory model was optimized by the road node arrival speed, considering the impact of road conditions on travel time. The node arrival speed was modeled as time series and predicted in the short-term future by combining the LSTM neural network model to construct a spatiotemporally continuous speed trajectory, to estimate the link’s travel time. The method was tested on an actual road and gave considerably improved estimates and results compared to the original method, using LSTM neural network to predict node departure speed and the technique using node arrival speed. According to experimental results, the proposed method performs well in both smooth and crowded traffic circumstances, serving as a benchmark for precise real-time travel time estimation on urban roads.

RFID E-plate Data based Travel Time Reliability Estimation of Urban Expressway

Travel time distribution shows the aggregation degree of vehicle travel time in road network, and travel time reliability is the key index to measure the aggregation degree of travel time. In order to quantitatively evaluate the fluctuation of travel time of expressway vehicles, it is necessary to establish an estimation method of travel time reliability of expressway. Travel time reliability estimation needs to obtain travel time data of vehicles. Traditional Global Position System (GPS) and video detection methods are easily affected by external factors such as weather and environment, and the description of travel time distribution is not comprehensive under the condition of low permeability. On the other hand, in the evaluation of travel time reliability, although there are lots of travel time probability distribution models, the correlation of vehicle travel time between links is ignored when calculating travel time reliability. In order to estimate travel time reliability more accurately, based on Radio Frequency Identification (RFID) electronic license plate (E-plate) data, this paper proposes a path travel time reliability evaluation model considering travel time correlation between links. First, the RFID readers installed on the gantry of expressway acquires data from the electronic tags deployed on vehicles. Secondly, a percentile travel time estimation method is proposed based on Cornish-Fisher expansion and the skewness of travel time distribution. Finally, considering the reliability estimation of travel time under the correlation of links, the estimated value is closer to the true value. The experimental results show that the travel time reliability estimation method considering the correlation between links improves the accuracy of estimation in the expressway network environment.

Analysis on Link Travel Time Estimation considering Time Headway Based on Urban Road RFID Data

In this paper, the calculation method of the link travel time is firstly analysed in the continuous traffic flow by using the detection data collected when vehicles pass through urban links, and a theoretical derivation formula for estimating link travel time is proposed by considering the typical vehicle travel time and the time headway deviation upstream and downstream of the links as the main parameters. A typical vehicle analysis method based on link travel time similarity is proposed, and the theoretical formula is optimized, respectively. Then, an estimation formula based on maximum travel time similarity and an estimation formula based on maximum travel time confidence interval similarity are proposed, respectively. Finally, when analysing the fitting conditions, the collected data from urban roads in Nanjing are used to verify the proposed travel time estimation method based on the radio frequency identification devices. The results show that time headway deviation converges to zero when the hourly vehicle volume is more than 20 veh/h in the certain flow direction, and there are more positive and negative fluctuations when the hourly vehicle volume is less than 10 veh/h in the certain flow direction. The accuracy of the proposed improved method based on typical vehicle travel time estimation is significantly improved by considering the typical vehicle travel time, and typical vehicles on the road segment mainly exist at the tail of the traffic platoon in the corresponding period.

Emergency response facility location in transportation networks: A literature review

Emergency response activity relies on transportation networks. Emergency facility location interacts with transportation networks clearly. This review is aimed to provide a combined framework for emergency facility location in transportation networks. The article reveals emergency response activities research clusters, issues, and objectives according to keywords co-occurrence analysis. Four classes of spatial separation models in transportation networks, including distance, routing, accessibility, and travel time are introduced. The stochastic and time-dependent characteristics of travel time are described. Travel time estimation and prediction method, travel time under emergency vehicle preemption, transportation network equilibrium method, and travel time in degradable networks are demonstrated. The emergency facilities location models interact with transportation networks, involving location-routing model, location models embedded with accessibility, location models embedded with travel time, and location models employing mathematical program with equilibrium constraints are reviewed. We then point out the-state-of-art challenges: ilities-oriented, evolution landscape and sequential decision modelling, data-driven optimization approach, and machine learning-based algorithms. • Emergency response facility location problem in transportation networks is reviewed for taxonomy purpose. • Travel time estimation and forecast methods in various scenarios are summarized. • Emergency facility location models in transportation networks are summarized and categorized. • Ilities-oriented, sequential decision modeling, data-driven optimization, and machine learning algorithm are to be studied.

Sparse Travel Time Estimation from Streaming Data

We address two shortcomings in online travel time estimation methods for congested urban traffic. The first shortcoming is related to the determination of the number of mixture modes, which can change dynamically within a day and from day to day. The second shortcoming is the widespread use of Gaussian probability densities as mixture components. Gaussian densities fail to capture the positive skew in travel time distributions, and consequently, large numbers of mixture components are needed for reasonable fitting accuracy when applied as mixture components. They also assign positive probabilities to negative travel times. To address these issues, this paper derives a mixture distribution with Gamma component densities, which are asymmetric and supported on the positive numbers. We use sparse estimation techniques to ensure parsimonious models and propose a generalization of Gamma mixture densities using Mittag–Leffler functions that provides enhanced fitting flexibility and improved parsimony. In order to accommodate within-day variability and allow for online implementation of the proposed methodology (i.e., fast computations on streaming travel time data), we introduce a recursive algorithm that efficiently updates the fitted distribution whenever new data become available. Experimental results using real-world travel time data illustrate the efficacy of the proposed methods.

Research on Highway Travel Time Estimation Method Based on Feature Matching

In order to solve the problem of inaccurate estimation of expressway travel time, this paper proposes a method of highway travel time estimation based on feature matching. Firstly, historical detector data is used as input to establish historical state library for each road segment, searching states which are similar to the current states. then estimate the travel time of the road segment in the state based on the K-nearest neighbor search algorithm. The advantage of this method is that complicated traffic modeling process is avoided, and the historical state library does not need to be updated in general. The method is based on vissim simulation of various traffic states to verify that the error is within the allowable range, and the results show that the method has strong stability.

Grouped travel time estimation in signalized arterials using point-to-point detectors

Travel time, an essential parameter in Advanced Traffic Management Systems and Advanced Traveller Information Systems, has been studied for decades of years, especially on freeways. However, the traffic flows in signalized arterials are more complex and stochastic, which enables arterial travel times to be multistate due to the interruptions from signal controls. Therefore, the signal effects need to be considered in the estimation of arterial travel times. In this paper, a grouped travel time estimation method is proposed to consider the signal interruptions and estimate the arterial travel times on a more micro level. With the data obtained from point-to-point detectors, the method analyzes vehicles’ traffic states before signals and recognizes those vehicles sharing the same states in both upstream and downstream signals as vehicle groups. Then the estimation problem is formulated as a clustering process to recognize the vehicle groups, and the grouped travel times are represented by the centers of vehicle groups. The field tests under ten different scenarios show that the grouped travel times can capture the multistate properties of the travel times in signalized arterials and represent the observed travel times of most vehicles within minor deviations.

Arterial travel time estimation method using SCATS traffic data based on KNN-LSSVR model

In order to improve the effect of estimating travel time and provide more precise and reliable traffic information to traffic management department and travelers, we proposed an arterial travel time estimation method using Sydney Coordinated Adaptive Traffic System traffic data based on K-nearest neighbor–least squares support vector regression model. First, the virtual time series is constructed by analyzing the characteristics of the inconsistent time intervals of Sydney Coordinated Adaptive Traffic System traffic data. Second, the K-nearest neighbor method was used to search the K similarity patterns matching the current traffic pattern and obtain K travel time data. Then, the least squares support vector regression model was used to perform travel time estimation. Finally, case validation is carried out using the measured data of Sydney Coordinated Adaptive Traffic System traffic control system. The estimation results demonstrate that the travel time estimation accuracy of proposed method outperforms the other two methods.

Freeway travel time estimation based on the general motors model: a genetic algorithm calibration framework

Travel time estimation plays an important role in freeway performance assessment and reliability management. Conventional estimation methods based on speed information do not consider the level of congestion during modelling, leading to unreliable estimations under congested conditions. This study presents a freeway travel time estimation method based on the general motors (GM) model, where the concept ‘virtual vehicle’ was proposed to apply macroscopic traffic flow data to the microscopic GM model. First, a GM-based travel time estimation (GMTTE) model was developed to estimate link travel time. Three sets of parameters l , m and α were separately used under free-flowing, congestion and transition conditions. Next, two corridor estimation models, namely GMTTE time-slice-based (GMTTE-TSB) model and GMTTE continuous-speed (GMTTE-CS) model, were proposed. To calibrate the three parameters, a genetic algorithm framework was developed. The proposed method was calibrated and evaluated using traffic data collected from seven freeway segments in the greater St. Louis, Missouri region. Results show that (i) the GMTTE-CS model outperformed the GMTTE-TSB model; (ii) the optimal parameters of the GMTTE-CS model under free-flowing and congested conditions are l = 1.1 , m = 2.0 , α = 8.0 and l = 1 , m = 0.1 , α = 8.0 , respectively; (iii) the GMTTE-CS model outperformed the instantaneous and time-slice models under congested conditions, while showing similar accuracies with the two models under free-flowing and status-transition conditions; and (iv) the optimal time interval was found to be 9–10 min for free-flowing conditions and 6–7 min for congested conditions.

Developing a Travel Time Estimation Method of Freeway Based on Floating Car Using Random Forests

Travel time of traffic flow is the basis of traffic guidance. To improve the estimation accuracy, a travel time estimation model based on Random Forests is proposed. 7 influence variables are viewed as candidates in this paper. Data obtained from VISSIM simulation are used to verify the model. Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. The result of variable importance shows that mean travel time of floating car t-f, traffic state parameter X, density of vehicle Kall, and median travel time of floating car tmenf are important variables affecting travel time of traffic flow; meanwhile other variables also have a certain influence on travel time. Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant.

Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

Real-time traffic status information provides good references for urban traffic control and management. Travel time is easy to understand and widely employed in representing traffic status. With significantly improved positioning accuracy and coverage, trajectory data collected from GPS-equipped probe vehicles have great potential for traffic state recognition. This paper presents a machine learning enabled travel time estimation method based on the GPS-equipped probe vehicles data. This research considers the spatial-temporal relevancy while solving the travel time allocation problem: the travel time of target segment might be associated with its previous travel times and/or the traffic states of nearby relevant segments. After data normalization and network clustering, an artificial neural network (ANN) algorithm considering such spatial-temporal relevancy was conducted to infer the travel time distribution among the traveled segments within one path. Furthermore, a weighted summation of the travel time estimation result from various trajectories was calculated to better represent the segment travel time in one time step. The proposed method was evaluated by evaluating the estimation results with automatic vehicle identification obtained ground truth. The experimental results illustrated that by utilizing the ANN to consider the spatial-temporal relevancy, the proposed method is effective and efficient in estimating the travel time.

Arterial link travel time estimation considering traffic signal delays using cellular handoff data

Link travel time is an important evaluation index of urban arterial traffic condition. It is traditionally estimated from loop detector data and GPS probe vehicle data. The former suffers from the coverage and maintenance issues. The latter has issues related to low penetration rate. Cellphone handoff data is one alternative that may address those deficiencies with its large coverage areas and continuous detection capabilities at low cost. Existing cellular probe vehicle based travel time estimation methods are primarily designed for freeway. Limited studies have been conducted for signalised arterials. Cellular handoff data detects the time lapse between two consecutive wireless handoff points. When vehicles with on-call cellphones traverse between two handoff points, their space mean speeds can be calculated by dividing the pre-calibrated distances by the time lapses. In this study, a new cellular handoff data based arterial travel time estimation model is proposed and compared with existing models. A hybrid traffic-and-wireless simulation network is built to simulate the handoff data generation and processing. Field data are collected at an arterial corridor in Chengdu, China to establish the baseline condition. The results from the proposed model show promising performance with detection errors of link travel times mostly within 10%.

Estimation of Travel Time Distributions in Urban Road Networks Using Low-Frequency Floating Car Data

Travel times in urban road networks are highly stochastic. However, most existing travel time estimation methods only estimate the mean travel times, while ignoring travel time variances. To this end, this paper proposes a robust travel time distribution estimation method to estimate both the mean and variance of travel times by using emerging low-frequency floating car data. Different from the existing studies, the path travel time distribution in this study is formulated as the sum of the deterministic link travel times and stochastic turning delays at intersections. Using this formulation, distinct travel time delays for different turning movements at the same intersection can be well captured. In this study, a speed estimation algorithm is developed to estimate the deterministic link travel times, and a distribution estimation algorithm is proposed to estimate the stochastic turning delays. Considering the low sampling rate of the floating car data, a weighted moving average algorithm is further developed for a robust estimation of the path travel time distribution. A real-world case study in Wuhan, China is carried out to validate the applicability of the proposed method. The results of the case study show that the proposed method can obtain a reliable and accurate estimation of path travel time distribution in congested urban road networks.

Developing travel time estimation methods using sparse GPS data

ABSTRACTExisting methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high-frequency data sources from specialist data providers, which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using “readily available” vehicle trajectory data from moving sensors such as buses, taxis, and logistical vehicles equipped with GPS in “near” real time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds, and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates, and time window lengths. Global positioning system (GPS) data from Interstate I-880 (California) for a total of 73 vehicles over 6 h were obtained from the University of California Berkeley's Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links, and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5-min time window length and for a GPS sampling frequency of 60 s.

Travel time estimation method for urban road based on traffic stream directions

ABSTRACTTraffic flow on a given link in an urban road network can be divided into several traffic streams, depending on their turning manoeuvres when entering and leaving the link. These traffic streams may experience various travel times due to multiple reasons, such as fluctuations in traffic demand/supply and stochastic arrivals/departures at signalised intersections. However, the current travel time estimation methods take traffic flow as a whole and produce a single estimation value. This approach can produce large errors. Furthermore, given the travel time information of each traffic stream, the results of dynamic traffic assignment models can be made much more accurate and the effect of signal controls improved. In this paper, a comparison analysis is conducted to verify the significant difference in link travel times of different traffic streams. Then, link travel time is redefined in consideration of traffic stream directions. This process is also successful in filtering noise, as shown in the numerical experiments. In addition, existing estimation methods cannot reflect real values or fluctuations of travel times in sampling intervals without any valid observational data. To solve this problem, a regression model is built and integrated into the travel time estimation model. Error analysis of several links on two different days demonstrates the improvements made by the model.

Intrinsic groundwater vulnerability determination at the aquifer scale: a methodology coupling travel time estimation and rating methods

Groundwater vulnerability has been subject of much research due to the valuable information it provides concerning groundwater protection and exploitation potential. Up to now, most groundwater vulnerability studies adopt subjective systems of rating the various factors and subsequently, their results are often ambiguous and contradicting. Within the present study a methodology for the estimation of intrinsic groundwater vulnerability at the aquifer scale is presented. The methodology is based on travel time estimation from specified sources of pollution to the aquifer. Besides the deterministic calculation of travel times, the methodology provides a rating system for each pollution source, based on its relative severity and the estimated threat that it poses to the aquifer. Therefore, it can be regarded as a hybrid method that couples the advantages provided by the physically based methods with those of the subjective rating systems. The methodology is applied to the Neon Sidirochorion aquifer, Northeastern Greece, an overexploited aquifer where river waters, sea waters and lake waters interact, causing groundwater quality deterioration to the aquifer. The results indicated that the proposed groundwater vulnerability assessment methodology is well capturing pollution related to saltwater intrusion and agricultural activities, while it is concluded that the conceptual model is significantly affecting the vulnerability assessment results and therefore has to be previously developed.

A Dynamic Travel Time Estimation Model Based on Connected Vehicles

With advances in connected vehicle technology, dynamic vehicle route guidance models gradually become indispensable equipment for drivers. Traditional route guidance models are designed to direct a vehicle along the shortest path from the origin to the destination without considering the dynamic traffic information. In this paper a dynamic travel time estimation model is presented which can collect and distribute traffic data based on the connected vehicles. To estimate the real-time travel time more accurately, a road link dynamic dividing algorithm is proposed. The efficiency of the model is confirmed by simulations, and the experiment results prove the effectiveness of the travel time estimation method.