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  • 1.
    Cumbane, Silvino Pedro
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Yang, Can
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    A Framework for Traffic Prediction Integrated with Deep Learning2019Conference paper (Refereed)
    Abstract [en]

    City-scale traffic prediction is an important task for public safety, traffic management, and deployment of intelligent transportation systems. Many approaches have been proposed to address traffic prediction task using machine learning techniques. In this paper, we present a framework to help on addressing the task at hand (density-, traffic flow- and origin-destination flow predictions) considering data type, features, deep learning techniques such as Convolutional Neural Networks (CNNs), e.g., Autoencoder, Recurrent Neural Networks (RNNs), e.g., Long Short Term Memory (LSTM), and Graph Convolutional Networks (GCNs). An autoencoder model is designed in this paper to predict traffic density based on historical data. Experiments on real-world taxi order data demonstrate the effectiveness of the model.

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    Traffic_Prediction_Framework
  • 2.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Yang, Can
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Scalable Detection of Traffic Congestion from Massive Floating Car Data Streams2015Conference paper (Refereed)
    Abstract [en]

    Motivated by the high utility and growing availability of Floating Car Data (FCD) streams for traffic congestion modeling and subsequent traffic congestion-related intelligent traffic management tasks, this paper proposes a grid-based, time-inhomogeneous model and method for the detection of congestion from large FCD streams. Furthermore, the paper proposes a simple but effective, high-level implementation of the method using off-the-shelf relational database technology that can readily be ported to Big Data processing frameworks. Empirical evaluations on millions of real-world taxi trajectories show that 1) the spatio-temporal distribution and clustering of the detected congestions are reasonable and 2) the method and its prototype implementation scale linearly with the input size and the geographical level of detail / spatio-temporal resolution of the model.

  • 3.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.
    Yang, Can
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    The potential of route based ERS network optimization2020In: TRA2020 Book of Abstracts: Proceedings of 8th Transport Research Arena TRA 2020, April 27-30, 2020, Helsinki, Finland / [ed] TRA, 2020, p. 1-12, article id 960Conference paper (Refereed)
    Abstract [en]

    The large scale deployment of Electric Road Systems (ERS) is a necessary and viable choice for reaching the emission reduction targets in the road-bound heavy freight sector. The per-kilometer infrastructure development cost of ERS is large, thus selecting segments that yield a high utility is important. According to a newly introduced concept, the electrification utility of a segment in a network is highly dependent on the freight routes-, the powertrain technology-, the energy supply and demand- and the transport loads of the vehicles as well as the topographic aspects- and traffic state of the road network. This paper explains these concepts and aspects and provides first empirical evidence about the potential of route based ERS network optimization that takes these aspects into consideration. Results show that the potential cost savings are up to 75%, which for national expressway networks is estimated to be in the range of 120M€ to 8,520M€.

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    Potential_of_RENO_ Gidofalvi_TRA2020
  • 4.
    Gidofalvi, Gyözö
    et al.
    KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL. KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Yang, Can
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    The potential of route based ERS network optimization: Transport demand optimized electric road placement2020Conference paper (Refereed)
    Abstract [en]

    Background

    The large scale deployment of Electric Road Systems (ERS) is a necessary and viable choice for reaching the emission reduction targets in the road-bound heavy freight sector. The per-kilometer infrastructure development cost of ERS is large, thus selecting segments that yield a high utility is important.

    Current methods that utilize freight routes try to optimize the ERS network infrastructure based on the amount of freight routes that include a road segment / link based on select link analysis which, based on network assignment models / assumptions or real data, provides information of where traffic comes from and goes to at selected links, i.e., it provides the spatial distribution and origin–destination (OD) pair composition of aggregate link flows.

    However, as it is suggested in a recent pre-study [1], it is not sufficient to select segments simply based on the number of routes that include the segment or based on the OD-information. In particular, different vehicle-powertrain-onboard and road charging technologies have different energy consumption, energy storage and charging characteristics. The energy consumption and charging characteristics of these vehicle-energy-technology configurations are also heavily affected by the load that they carry as well as the surface and 3D geometry of the roads that they are operated on. Finally, last but not least, as the vehicles have a finite energy storage and an objective to complete maximal part of their routes on electric energy, the electrification utility of segments / links in a network are not independent of one another, but largely depend on which part of the routes include the segments.

    Method

    To take the aforementioned aspects into account, the proposed methodology, adopts the newly introduced concept of Route Based Electrification Utility (RBEU) [1], which can be informally defined as the extra transport work that can be performed in electric operations because of the electrification of the segment in question. Given an infrastructure budget of N segments, to find the N segments that maximizes the electrification utility, the proposed Route based ERS Network Optimization (RENO) method first compresses and indexes the massive input set of vehicle trajectories (routes) and then utilizes this data structure to incrementally add segments to the partial solution that maximize the RBEU of the selection. To take into consideration of vehicle load, speed and road geometries two approaches are proposed: one based on GIS data and simulations and one based on vehicle fuel consumption as a proxy.

    Results and Conclusions

    Empirical evaluations on a real word data set 1) illustrate the superiority of the RENO method over the segment based optimization approach for a wide range of electrification scenarios and 2) analyze the characteristics of the solutions found by the RENO methodology. The potential infrastructure cost savings resulting from RENO are up to 75%, which for national expressway networks is estimated to be in the range of 120M€ (Sweden) to 8,520M€ (China).

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    fulltext
  • 5.
    Yang, Can
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Discovering Contiguous Sequential Patterns in Network-Constrained Movement2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    A large proportion of movement in urban area is constrained to a road network such as pedestrian, bicycle and vehicle. That movement information is commonly collected by Global Positioning System (GPS) sensor, which has generated large collections of trajectories. A contiguous sequential pattern (CSP) in these trajectories represents a certain number of objects traversing a sequence of spatially contiguous edges in the network, which is an intuitive way to study regularities in network-constrained movement. CSPs are closely related to route choices and traffic flows and can be useful in travel demand modeling and transportation planning. However, the efficient and scalable extraction of CSPs and effective visualization of the heavily overlapping CSPs are remaining challenges.

    To address these challenges, the thesis develops two algorithms and a visual analytics system. Firstly, a fast map matching (FMM) algorithm is designed for matching a noisy trajectory to a sequence of edges traversed by the object with a high performance. Secondly, an algorithm called bidirectional pruning based closed contiguous sequential pattern mining (BP-CCSM) is developed to extract sequential patterns with closeness and contiguity constraint from the map matched trajectories. Finally, a visual analytics system called sequential pattern explorer for trajectories (SPET) is designed for interactive mining and visualization of CSPs in a large collection of trajectories.

    Extensive experiments are performed on a real-world taxi trip GPS dataset to evaluate the algorithms and visual analytics system. The results demonstrate that FMM achieves a superior performance by replacing repeated routing queries with hash table lookups. BP-CCSM considerably outperforms three state-of-the-art algorithms in terms of running time and memory consumption. SPET enables the user to efficiently and conveniently explore spatial and temporal variations of CSPs in network-constrained movement.

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    fulltext
  • 6.
    Yang, Can
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Efficient Map Matching and Discovery of Frequent and Dominant Movement Patterns in GPS Trajectory Data2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The wide deployment of Global Positioning System (GPS) sensors for movement data collection has enabled a wide range of applications in transportation and urban planning. Frequent and dominant movement patterns embedded in GPS trajectory data provide valuable knowledge including the spatial and temporal distribution of frequent routes selected by the tracked objects and the regular movement behavior in certain regions. Discovering frequent and dominant movement patterns embedded in GPS trajectory data needs to address several tasks including (1) matching noisy trajectories to the road network (referred as map matching), (2) extracting frequent and dominant movement patterns, and (3) retrieving the distribution of these patterns over user-specified attribute (e.g., timestamp, travel mode, etc.). These tasks confront several challenges in observation error, efficiency and large pattern search space.

    To address those challenges, this thesis develops a set of algorithms and tools for efficient map matching and discovery of frequent and dominant movement patterns in GPS trajectory data. More specifically, two map matching algorithms are first developed, which improve the performance by precomputation and A-star search. Subsequently, a frequent route is extracted from map matched trajectories as a Contiguous Sequential Pattern (CSP). A novel CSP mining algorithm is developed by performing bidirectional pruning to efficiently search CSP and reduce redundancy in the result. After that, an efficient CSP comparison algorithm is developed to extend the bidirectional pruning to compare multiple sets of CSP. By comparing CSP mined from trajectories partitioned by a user-specified attribute, the distribution of frequent routes in the attribute space can be obtained. Finally, Regional Dominant Movement Pattern (RDMP) in trajectory data is discovered as regions where most of the objects follow a specific pattern. A novel movement feature descriptor called Directional Flow Image (DFI) is proposed to capture local directional movement information of trajectories in a multiple channel image and a convolutional neural network model is designed for DFI classification and RDMP detection.

    Comprehensive experiments on both real-world and synthetic GPS datasets demonstrate the efficiency of the proposed algorithms as well as their superiority over state-of-the-art methods. The two map matching algorithms achieve considerable performance in matching densely sampled GPS data to small scale network and sparsely sampled GPS data to large scale network respectively. The CSP mining and comparison algorithms significantly outperform their competitors and effectively retrieve both spatial and temporal distribution of frequent routes. The RDMP detection method can robustly discover ten classes of commonly encountered RDMP in real-world trajectory data. The proposed methods in this thesis collectively provide an effective solution for answering sophisticated queries concerning frequent and dominant movement patterns in GPS trajectory data.

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    fulltext
  • 7.
    Yang, Can
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Efficient Comparison of Contiguous Sequential Patterns in GPS Trajectory Data by Bidirectional PruningManuscript (preprint) (Other academic)
    Abstract [en]

    Contiguous sequential pattern (CSP) represents a contiguous subsequence that frequently appearing in a sequence database. When applies CSP mining to GPS trajectory data collected from vehicles, the discovered CSP reveals a frequent route in a road network. Retrieving the distribution of frequent routes over time or among different groups of users can be formulated as a CSP comparison problem, which has important application in intelligent transportation system. However, it confronts several challenges including compressed CSP information, large search space and redundancy in the output. To address those challenges, this paper proposes a Bidirectional pruning based CSP Comparison (BCSPC) algorithm by employing three tree structures to efficiently search and compare multiple sets of CSP. Comprehensive experiments are performed on a big public real-world GPS trajectory dataset where BCSPC is compared with two other algorithms CSP-FP-tree and BIDE. The results show that when CSP is mined with a small support, BCSPC considerably outperforms two other algorithms with speed improved by 2 to 10 times and memory significantly reduced. The visualization of comparison results demonstrates the effectiveness of BCSPC in answering sophisticated queries regarding the temporal distribution of frequent routes embedded in GPS trajectory data. 

  • 8.
    Yang, Can
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. KTH, Royal Institute of Technology in Sweden.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Fast map matching, an algorithm integrating hidden Markov model with precomputation2017In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, p. 1-24Article in journal (Refereed)
    Abstract [en]

    Wide deployment of global positioning system (GPS) sensors has generated a large amount of data with numerous applications in transportation research. Due to the observation error, a map matching (MM) process is commonly performed to infer a path on a road network from a noisy GPS trajectory. The increasing data volume calls for the design of efficient and scalable MM algorithms. This article presents fast map matching (FMM), an algorithm integrating hidden Markov model with precomputation, and provides an open-source implementation. An upper bounded origin-destination table is precomputed to store all pairs of shortest paths within a certain length in the road network. As a benefit, repeated routing queries known as the bottleneck of MM are replaced with hash table search. Additionally, several degenerate cases and a problem of reverse movement are identified and addressed in FMM. Experiments on a large collection of real-world taxi trip trajectories demonstrate that FMM has achieved a considerable single-processor MM speed of 25,000–45,000 points/second varying with the output mode. Investigation on the running time of different steps in FMM reveals that after precomputation is employed, the new bottleneck is located in candidate search, and more specifically, the projection of a GPS point to the polyline of a road edge. Reverse movement in the result is also effectively reduced by applying a penalty.

  • 9.
    Yang, Can
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. KTH, Royal Institute of Technology in Sweden.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Matching Sparse GPS Data to Large Scale Road Network Accelerated by AStar SearchManuscript (preprint) (Other academic)
    Abstract [en]

    Map matching (MM) infers the path on a road network from a noisy GPS trajectory. It is an important preprocessing procedure in many knowledge discovery tasks concerning GPS data. The bottleneck of MM has been widely recognized as the large number of repeated routing queries. Previous MM methods accelerated by precomputation can effectively address this issue but it can consume extremely high memory when the GPS dataset covers a large scale road network containing millions of edges. This paper studies the problem of matching sparse GPS data to a large scale road network. In addition to the repeated routing queries, another bottleneck of MM is identified as sequential search constraint where candidates of the current GPS observation must be evaluated before examining candidates of the next. With the objective to addressing those two types of bottleneck, this paper proposes an AStar Search based MM (ASMM) algorithm that is applicable for matching sparse GPS data to large scale road network. ASMM transforms MM into a candidate waynode routing problem and introduces level-associated distance label to avoid performing repeated routing queries. Moreover, the sequential search constraint is addressed by employing AStar search strategy. Comprehensive experiments on synthetic GPS dataset demonstrate the superiority of ASMM over three state-of-the-art algorithms. When matching sparse GPS data to a large scale network, the speed of ASMM is about 5 to 20 times of its competitors. When matching to a small scale network, ASMM is still competitive in achieving a similar speed as the fastest algorithm but consumes only 10% of memory. 

  • 10.
    Yang, Can
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gidofalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Mining and visual exploration of closed contiguous sequential patterns in trajectories2018In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824, Vol. 32, no 7, p. 1282-1303Article in journal (Refereed)
    Abstract [en]

    Large collections of trajectories provide rich insight into movement patterns of the tracked objects. By map matching trajectories to a road network as sequences of road edge IDs, contiguous sequential patterns can be extracted as a certain number of objects traversing a specific path, which provides valuable information in travel demand modeling and transportation planning. Mining and visualization of such patterns still face challenges in efficiency, scalability, and visual cluttering of patterns. To address these challenges, this article firstly proposes a Bidirectional Pruning based Closed Contiguous Sequential pattern Mining (BP-CCSM) algorithm. By employing tree structures to create partitions of input sequences and candidate patterns, closeness can be checked efficiently by comparing nodes in a tree. Secondly, a system called Sequential Pattern Explorer for Trajectories (SPET) is built for spatial and temporal exploration of the mined patterns. Two types of maps are designed where a conventional traffic map gives an overview of the movement patterns and a dynamic offset map presents detailed information according to user-specified filters. Extensive experiments are performed in this article. BP-CCSM is compared with three other state-of-the-art algorithms on two datasets: a small public dataset containing clickstreams from an e-commerce and a large global positioning system dataset with more than 600,000 taxi trip trajectories. The results show that BP-CCSM considerably outperforms three other algorithms in terms of running time and memory consumption. Besides, SPET provides an efficient and convenient way to inspect spatial and temporal variations in closed contiguous sequential patterns from a large number of trajectories.

  • 11.
    Yang, Can
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Gidófalvi, Gyözö
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
    Detecting regional dominant movement patterns in trajectory data with a convolutional neural network2019In: International Journal of Geographical Information Science, ISSN 1365-8816, E-ISSN 1365-8824Article in journal (Refereed)
    Abstract [en]

    Detecting movement patterns with complicated spatial or temporal characteristics is a challenge. The past decade has witnessed the success of deep learning in processing image, voice and text data. However, its application in movement pattern detection is not fully exploited. To address the research gap, this paper develops a deep learning approach to detect regional dominant movement patterns (RDMP) in trajectory data. Specifically, a novel feature descriptor called directional flow image (DFI) is firstly proposed to store the local directional movement information in trajectory data. A DFI classification model called TRNet is designed based on convolutional neural network. The model is then trained with a synthetic trajectory dataset covering 11 classes of commonly encountered movement patterns in reality. Finally, a sliding window detector is built to detect RDMP at multiple scales and a clustering-based merging method is proposed to prune the redundant detection results. Training of TRNet on the synthetic dataset achieves considerably high accuracy. Experiments on a real-world taxi trajectory dataset further demonstrate the effectiveness and efficiency of the proposed approach in discovering complex movement patterns in trajectory data.

  • 12.
    Yang, Can
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. KTH, Royal Institute of Technology in Sweden.
    Gyözö, Gidofalvi
    KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. KTH, Royal Institute of Technology in Sweden.
    Interactive Visual Exploration of Most LikelyMovements2016In: Geospatial data in a changing world selected papers of the 19th AGILE Conference on Geographic Information Science, Springer, 2016Conference paper (Refereed)
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    fulltext
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