Travel time prediction using speed profiles for road network of Croatia

Erdelić, Tomislav; Ravlic, Martina; Carić, Tonči

doi:10.1109/elmar.2016.7731763

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Most of the E-VRP research considers static conditions on the road network. The traffic states change recurrently, depending on the time of the day, day of the week, and season, or nonrecurrently when a traffic incident occurs, such as an accident [132][133][134]. TD-VRP routes a fleet of vehicles by taking into account variable travel time on the road network [135,136].…”

Section: Dynamic Traffic Conditionsmentioning

confidence: 99%

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Erdelić

Carić

2019

Journal of Advanced Transportation

175

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In order to ensure high-quality and on-time delivery in logistic distribution processes, it is necessary to efficiently manage the delivery fleet. Nowadays, due to the new policies and regulations related to greenhouse gas emission in the transport sector, logistic companies are paying higher penalties for each emission gram of CO2/km. With electric vehicle market penetration, many companies are evaluating the integration of electric vehicles in their fleet, as they do not have local greenhouse gas emissions, produce minimal noise, and are independent of the fluctuating oil price. The well-researched vehicle routing problem (VRP) is extended to the electric vehicle routing problem (E-VRP), which takes into account specific characteristics of electric vehicles. In this paper, a literature review on recent developments regarding the E-VRP is presented. The challenges that emerged with the integration of electric vehicles in the delivery processes are described, together with electric vehicle characteristics and recent energy consumption models. Several variants of the E-VRP and related problems are observed. To cope with the new routing challenges in E-VRP, efficient VRP heuristics and metaheuristics had to be adapted. An overview of the state-of-the-art procedures for solving the E-VRP and related problems is presented.

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Section: Dynamic Traffic Conditionsmentioning

confidence: 99%

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

Erdelić

Carić

2019

Journal of Advanced Transportation

175

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“…Vector representation of traffic data in the form of a time series is one of the most common data modeling techniques [17]. The change of traffic parameter under observation with dimensions 1 × n is examined through a daily profile in defined n time intervals.…”

Section: Data Modeling Techniquesmentioning

confidence: 99%

“…Most of the authors represent traffic data as a time series vector v ∈ R 1×n [17] or a two-dimensional matrix M ∈ R m×n [22]. Dimensions m and n refer to the numbers of the road network segments (the spatial component) and the number of time intervals (the temporal component) of the observed road network.…”

Section: Speed Transition Matrixmentioning

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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“…As link lengths were known in advance, it was then possible to compute the speed. Finally, the mean speed was calculated using all speeds in the same five-minute interval on that link [23]. In order to solve the time dependent SPP, the time dependent Dijkstra algorithm was used.…”

Section: Speed Profilesmentioning

confidence: 99%

Using Congestion Zones for Solving the Time Dependent Vehicle Routing Problem

Carić

Fosin²

2020

PROMET

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This paper provides a framework for solving the Time Dependent Vehicle Routing Problem (TDVRP) by using historical data. The data are used to predict travel times during certain times of the day and derive zones of congestion that can be used by optimization algorithms. A combination of well-known algorithms was adapted to the time dependent setting and used to solve the real-world problems. The adapted algorithm outperforms the best-known results for TDVRP benchmarks. The proposed framework was applied to a real-world problem and results show a reduction in time delays in serving customers compared to the time independent case.

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Travel time prediction using speed profiles for road network of Croatia

Cited by 8 publications

References 13 publications

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

A Survey on the Electric Vehicle Routing Problem: Variants and Solution Approaches

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

Using Congestion Zones for Solving the Time Dependent Vehicle Routing Problem

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