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Journal ArticleDOI

Arterial Path-Level Travel-Time Estimation Using Machine-Learning Techniques

01 May 2017-Journal of Computing in Civil Engineering (American Society of Civil Engineers (ASCE))-Vol. 31, Iss: 3, pp 04016070
TL;DR: A methodology for travel-time prediction on urban arterial networks using data from global positioning system (GPS) probe vehicles, under Indian traffic conditions, is presented.
Abstract: This study presents a methodology for travel-time prediction on urban arterial networks using data from global positioning system (GPS) probe vehicles, under Indian traffic conditions. Give...
Citations
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Journal ArticleDOI
TL;DR: The new concept of consensual 3D speed maps allows the essence out of large amounts of link speed observations and reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.
Abstract: In this paper, we investigate the day-to-day regularity of urban congestion patterns. We first partition link speed data every 10 min into 3D clusters that propose a parsimonious sketch of the congestion pulse. We then gather days with similar patterns and use consensus clustering methods to produce a unique global pattern that fits multiple days, uncovering the day-to-day regularity. We show that the network of Amsterdam over 35 days can be synthesized into only 4 consensual 3D speed maps with 9 clusters. This paves the way for a cutting-edge systematic method for travel time predictions in cities. By matching the current observation to historical consensual 3D speed maps, we design an efficient real-time method that successfully predicts 84% trips travel times with an error margin below 25%. The new concept of consensual 3D speed maps allows us to extract the essence out of large amounts of link speed observations and as a result reveals a global and previously mostly hidden picture of traffic dynamics at the whole city scale, which may be more regular and predictable than expected.

221 citations

Journal ArticleDOI
Kejun Long, Wukai Yao, Jian Gu, Wei Wu, Lee D. Han 
TL;DR: The Artificial Fish Swarm algorithm is applied to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C, and the results show that the accuracy of the optimized S VM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM models, respectively.
Abstract: Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter e, and penalty parameter C. We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.

9 citations

DOI
27 Feb 2020
TL;DR: This thesis develops a series of efficient data-driven methods for extracting the mobility patterns of large-scale metropolitan networks and explores some of their applications.
Abstract: Cities are complex, dynamic and ever-evolving. We need to understand how these cities work in order to predict, control or optimize their operations. We have identified some open issues related to network and data complexity that need to be solved to build feasible methods for these purposes. To this end, we first build multiscale graphs automatically to address a problem that is becoming increasingly relevant in the age of big data, where reducing the network complexity could easily determine the viability of the research in real-world applications. Next, we propose different methods from different fields to extract the essence of network dynamics from the vast amount of spatiotemporal traffic data. One such method is a new way of looking at traffic patterns, combining the field of pattern recognition - with a focus on computer vision - with the traffic domain. The inspiration comes from the fact that humans are the most sophisticated pattern recognizer in the world and we use specific visual features to recognize different complex patterns and we explore if these features can also be used to recognize traffic patterns. Finally, we explore different applications of such mobility patterns such as revealing the unknown correlation between supply and demand patterns, evaluating the scalability of the proposed approach by applying the method to the entire Dutch highway network and transferability by building similar network patterns for public transport networks. Thus, this thesis develops a series of efficient data-driven methods for extracting the mobility patterns of large-scale metropolitan networks and explore some of their applications. With the increasing availability of data in the transport domain, the Achilles heel is not data scarcity anymore but rather extracting insights from this massive amount of data. This thesis is a step forward in solving this complex problem by leveraging the increased acceptance of using machine learning as a worthy and effective method for network-wide analysis of traffic patterns.

8 citations


Cites background from "Arterial Path-Level Travel-Time Est..."

  • ...parameterized mathematical models such as (generalized) linear regression[137, 138]; kriging [139]; support vector regression [140]; random forest[141]; Bayesian networks[142]; artificial neural networks, e....

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Journal ArticleDOI
TL;DR: The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue.
Abstract: Surface transportation has evolved through technology advancements using parallel knowledge areas such as machine learning (ML). However, the transportation industry has not yet taken full advantage of ML. To evaluate this gap, we utilized a literature review approach to locate, categorize, and synthesize the principal concepts of research papers regarding surface transportation systems using ML algorithms, and we then decomposed them into their fundamental elements. We explored more than 100 articles, literature review papers, and books. The results show that 74% of the papers concentrate on forecasting, while multilayer perceptions, long short-term memory, random forest, supporting vector machine, XGBoost, and deep convolutional neural networks are the most preferred ML algorithms. However, sophisticated ML algorithms have been minimally used. The root-cause analysis revealed a lack of effective collaboration between the ML and transportation experts, resulting in the most accessible transportation applications being used as a case study to test or enhance a given ML algorithm and not necessarily to enhance a mobility or safety issue. Additionally, the transportation community does not define transportation issues clearly and does not provide publicly available transportation datasets. The transportation sector must offer an open-source platform to showcase the sector’s concerns and build spatiotemporal datasets for ML experts to accelerate technology advancements.

4 citations

Journal ArticleDOI
TL;DR: The performance of the proposed Advanced Time-Space Discterization (AdTSD) method was evaluated with real field data and compared with existing approaches and results show that AdTSD approach was able to perform better than historical average approach with an advantage up to 11% and 5% compared to Base Time Space Discretization (B TSD) approach.
Abstract: Travel time is a variable that varies over both time and space. Hence, an ideal formulation should be able to capture its evolution over time and space. A mathematical representation capturing such variations was formulated from first principles, using the concept of conservation of vehicles. The availability of position and speed data obtained from GPS enabled buses provide motivation to rewrite the conservation equation in terms of speed alone. As the number of vehicles is discrete, the speed-based equation was discretized using Godunov scheme and used in the prediction scheme that was based on the Kalman filter. With a limited fleet size having an average headway of 30 min, availability of travel time data at small interval that satisfy the requirement of stability of numerical solution possess a big challenge. To address this issue, a continuous speed fill matrix spatially and temporally was developed with the help of historic data and used in this study. The performance of the proposed Advanced Time-Space Discterization (AdTSD) method was evaluated with real field data and compared with existing approaches. Results show that AdTSD approach was able to perform better than historical average approach with an advantage up to 11% and 5% compared to Base Time Space Discretization (BTSD) approach. Also, from the results it was observed that the maximum deviation in prediction was in the range of 2–3 min when it is predicted 10 km ahead and the error is close to zero when it is predicted a section ahead i.e. when the bus is close to a bus stop, indicating that the prediction accuracy achieved is suitable for real field implementation.

3 citations

References
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Journal ArticleDOI
TL;DR: Two techniques are presented for real-time measurement of vehicledelay and queue length at a signalized intersection, and these automated delay and queue estimates are compared with manually ground-truthed measurement.
Abstract: Vehicle delay and queue length are quantitative measures of intersection performance. The technological advancement in the field of vehicle sensors and traffic controllers has reached a point where it has enabled efficient measurement of these performance measures. Two techniques are presented for real-time measurement of vehicle delay and queue length at a signalized intersection, and these automated delay and queue estimates are compared with manually ground-truthed measurement. These techniques were evaluated at an instrumented intersection in Noblesville, Indiana. The root-mean-square error by both techniques was below 0.7 veh-s for estimation of average delay and less than 0.15 vehicle for estimation of average maximum queue length, both on a cycle-by-cycle basis.

182 citations

Journal ArticleDOI
TL;DR: A hybrid modeling framework for estimating and predicting arterial traffic conditions using streaming GPS probe data and machine learning framework to both learn static parameters of the roadways as well as to estimate and predict travel times through the arterial network.
Abstract: This article presents a hybrid modeling framework for estimating and predicting arterial traffic conditions using streaming global positioning system (GPS) probe data. The model is based on a well-established theory of traffic flow through signalized intersections and is combined with a machine learning framework to both learn static parameters of the roadways (such as free flow velocity or traffic signal parameters) as well as to estimate and predict travel times through the arterial network. The machine learning component of the approach uses the significant amount of historical data collected by the Mobile Millennium system since March 2009 with over 500 probe vehicles reporting their position once per minute in San Francisco, CA. The hybrid model provides a distinct advantage over pure statistical or pure traffic theory models in that it is robust to noisy data (due to the large volumes of historical data) and it produces forecasts using traffic flow theory principles consistent with the physics of traffic. Validation of the model is performed in two different ways. First, a large scale test of the model is performed by splitting the data source into two sets, using the first to produce the estimates and the second to validate them. Second, an alternate validation approach is presented. It consists of a 3-day experiment in which GPS data was collected once per second from 20 drivers on four routes through San Francisco, allowing for precise calculation of actual travel times. The model is run by down-sampling the data and validated using the travel times from these 20 drivers. The results indicate that this approach is a significant step forward in estimating traffic states throughout the arterial network using a relatively small amount of real-time data. The estimates from the model are compared to those given by a data-driven baseline algorithm, for which the authors achieve a 16% improvement in terms of the root mean squared error of travel time estimates. The primary reason for success is the reliance on a flow model of traffic, which ensures that estimates are consistent with the physics of traffic.

174 citations

Journal ArticleDOI
TL;DR: An algorithm for solving the problem of decomposing the traversal time to times taken to traverse individual road segments on the route and defines a likelihood function that is maximized to solve for the most likely travel time for each road segment on the traversed route.
Abstract: In probe-based traffic monitoring systems, traffic conditions can be inferred based on the position data of a set of periodically polled probe vehicles. In such systems, the two consecutive polled positions do not necessarily correspond to the end points of individual links. Obtaining estimates of travel time at the individual link level requires the total traversal time (which is equal to the polling interval duration) be decomposed. This paper presents an algorithm for solving the problem of decomposing the traversal time to times taken to traverse individual road segments on the route. The proposed algorithm assumes minimal information about the network, namely network topography (i.e. links and nodes) and the free flow speed of each link. Unlike existing deterministic methods, the proposed solution algorithm defines a likelihood function that is maximized to solve for the most likely travel time for each road segment on the traversed route. The proposed scheme is evaluated using simulated data and compared to a benchmark deterministic method. The evaluation results suggest that the proposed method outperforms the bench mark method and on average improves the accuracy of the estimated link travel times by up to 90%.

130 citations

Journal ArticleDOI
TL;DR: A knowledge based real-time travel time prediction model which contains real- time and historical travel time predictors to discover traffic patterns from the raw data of location based services by data mining technique and transform them to travel time Prediction rules is proposed.
Abstract: Many approaches had been proposed for travel time prediction in these decades; most of them focus on the predicting the travel time on freeway or simple arterial network. Travel time prediction for urban network in real time is hard to achieve for several reasons: complexity and path routing problem in urban network, unavailability of real-time sensor data, spatiotemporal data coverage problem, and lacking real-time events consideration. In this paper, we propose a knowledge based real-time travel time prediction model which contains real-time and historical travel time predictors to discover traffic patterns from the raw data of location based services by data mining technique and transform them to travel time prediction rules. Besides, dynamic weight combination of the two predictors by meta-rules is proposed to provide a real-time traffic event response mechanism to enhance the precision of the travel time prediction.

108 citations