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...
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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
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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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
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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TL;DR: The proposed models, although lacking in mathematical elegance, are capable of providing the acceptable prediction accuracy at 3 time-steps ahead and are sufficiently detailed for both responsive signal control and intersection operations.
Abstract: To capture the complex nature of intersection queue dynamics, this study has explored the use of neural network models with data from extensive simulation experiments. The proposed models, although lacking in mathematical elegance, are capable of providing the acceptable prediction accuracy (more than 90%) at 3 time-steps ahead. As each time-step is as short as 3 s, the resulting information on queue evolution is sufficiently detailed for both responsive signal control and intersection operations. To accommodate the differences in available surveillance systems, this study has also investigated the most suitable neural network structure for each proposed queue model with extensive exploratory analyses.
61 citations
01 May 2012
TL;DR: Three non-parametric state-of-the-art regression methods are compared and it is shown that RF is the most promising approach from the three and possible to obtain more accurate results using PPR but with extra pre-processing work, namely on example selection and domain values definition.
Abstract: Long-term travel time prediction TTP can be an important planning tool for both freight transport and public transport companies. In both cases it is expected that the use of long-term TTP can improve the quality of the planned services by reducing the error between the actual and the planned travel times. However, for reasons that we try to stretch out along this paper, long-term TTP is almost not mentioned in the scientific literature. In this paper we discuss the relevance of this study and compare three non-parametric state-of-the-art regression methods: Projection Pursuit Regression PPR, Support Vector Machine SVM and Random Forests RF. For each one of these methods we study the best combination of input parameters. We also study the impact of different methods for the pre-processing tasks feature selection, example selection and domain values definition in the accuracy of those algorithms. We use bus travel time's data from a bus dispatch system. From an off-the-shelf point-of-view, our experiments show that RF is the most promising approach from the three we have tested. However, it is possible to obtain more accurate results using PPR but with extra pre-processing work, namely on example selection and domain values definition.
57 citations
TL;DR: In this article, the authors prove some new fixed point theorems for weak commuting mapping on complete metric space, which generalize several corresponding relations in metric space of weak commutative mapping.
Abstract: In this paper we prove some new fixed point theorems for weak commuting mapping on complete metric space. Our results generalize several corresponding relations in metric space of weak commuting mapping.
44 citations
01 Dec 2003
TL;DR: The results reported in this paper indicate that pattern matching technique is capable of predicting travel time with a high degree of accuracy (90 to 95 percent) and clearly demonstrates the feasibility of usingPattern matching technique for travel time prediction using traffic detector data.
Abstract: This paper discusses an algorithm developed for short-term travel time prediction. The benefits of the travel time information provision have been documented, and range from spatio-temporal dispersal of traffic and less stressful driving to utilization of alternative modes of travel. Majority of present travel time information systems use instantaneous travel time (i.e. summing of travel time information derived from velocity measurements at different sections of road simultaneously.) Instantaneous travel time information requires less computational effort but accuracy decreases with the onset of congestion. The pattern matching technique used in this research is based on the assumption that traffic scenarios similar to present traffic condition may have occurred before. Present traffic pattern is defined using velocity measurements from traffic detectors along the length of road up to one hour before the present time. Instead of using simple patterns, weighted patterns are used for defining traffic situations. A database of historical traffic situations is stored for searching the closest matched patterns and minimum squared difference is used as indicator of the closest matched patterns from historical database. Instead of selecting one most similar pattern, ten patterns are selected so that sudden changes in travel time prediction can be avoided. The results reported in this paper indicate that pattern matching technique is capable of predicting travel time with a high degree of accuracy (90 to 95 percent). This research clearly demonstrates the feasibility of using pattern matching technique for travel time prediction using traffic detector data.
37 citations
24 Oct 2005
TL;DR: A macroscopic model of urban link travel time prediction is developed based on measurements collected by single loop detectors that divides travel times into two components: link cruising times and intersection delays.
Abstract: Although there is an increasing demand for travel time prediction methods, the efforts put on urban streets are until now less than on freeways In this paper, a macroscopic model of urban link travel time prediction is developed based on measurements collected by single loop detectors This method divides travel times into two components: link cruising times and intersection delays For each component the mean prediction and prediction interval were investigated in order to provide a reliable prediction Prediction intervals around the mean prediction show the plausible range of travel times a vehicle is likely to encounter Based on simulation data, it is demonstrated that urban link travel time prediction can be executed by this promising method
32 citations