Using Congestion Zones for Solving the Time Dependent Vehicle Routing Problem

Carić, Tonči; Fosin, Juraj

doi:10.7307/ptt.v32i1.3296

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…In this work we considered two synthetic networks (synth and synthx) and seven realworld time-evolving networks: stockmarket (Costa, 2018), challengenet (Rayana and Akoglu, 2014), enron (Priebe et al, 2005), manufactoring (Michalski et al 2011), reality (Eagle and Pentland, 2006), biketrips 1 and zagrebtraffic (Carić and Fosin, 2020). In particular, stockmarket maps the correlation between assets in the stock market at each semester.…”

Section: Datasetsmentioning

confidence: 99%

“…biketrips is network of bike trips between stations in Washington so that an edge between two stations has a weight equivalent to the number of bike trips between those stations. Finally, zagrebtraffic is part of the road network of Zagreb whose edges are weighted by the relative average speed between the corresponding road intersections at a given time of working days, with summer months (July and August) excluded from the data (for a more detailed description of this network, the reader may refer to Carić and Fosin (2020)). All these networks are summarized in Table 2.…”

Section: Datasetsmentioning

confidence: 99%

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WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks

et al. 2021

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Densification events in time-evolving networks refer to instants in which the network density, that is, the number of edges, is substantially larger than in the remaining. These events can occur at a global level, involving the majority of the nodes in the network, or at a local level involving only a subset of nodes.While global densification events affect the overall structure of the network, the same does not hold in local densification events, which may remain undetectable by the existing detection methods. In order to address this issue, we propose WINdowed TENsor decomposition for Densification Event Detection (WINTENDED) for the detection and characterization of both global and local densification events. Our method combines a sliding window decomposition with statistical tools to capture the local dynamics of the network and automatically find the irregular behaviours. According to our experimental evaluation, WINTENDED is able to spot global densification events at least as accurately as its competitors, while also being able to find local densification events, on the contrary to its competitors.

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Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks

et al. 2021

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“…Results of a traffic state estimation are shown for the eight time intervals throughout a day. Time intervals are defined by [34,36] Table 1 represents the results for the traffic state estimation grouped into three classes. The results are shown while using the ratios between the number of classified transitions and the total number of transitions in the observed time interval.…”

Section: Traffic State Estimationmentioning

confidence: 99%

“…Traffic state estimation based on the STM can provide useful information regarding the congestion, and therefore routing through the less congested roads. The framework for solving the well-known routing problem Time-Dependent Vehicle Routing Problem, is presented in [36]. The authors used speed profiles to extract the congestion zones and quantified the congestion by computing the slowdown coefficients using the travel times.…”

Section: Routing Applicationsmentioning

confidence: 99%

Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

et al. 2020

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The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.

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Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method

et al. 2020

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Using Congestion Zones for Solving the Time Dependent Vehicle Routing Problem

Cited by 9 publications

References 23 publications

WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks

WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks

Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method

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