Analysis and Classification of the Vehicular Traffic Distribution in an Urban Area

Zambrano-Martinez, Jorge Luis; Calafate, Carlos T.; Soler, David; Cano, Juan-Carlos; Manzoni, Pietro

doi:10.1007/978-3-319-67910-5_10

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…In addition, we also know what happens with the second class of the curve shown in Figure 3 b, which represents an abnormal situation according to the traffic theory. To achieve this goal, it is necessary to use the Equation

presented in a previous work [ 11 ], to gradually increase the number of vehicles in our reference traffic scenario for Valencia until a higher saturation level is reached. This is achieved by assigning a variable number of additional vehicles to be injected at each traffic source location.…”

Section: Methodsmentioning

confidence: 99%

“…This way we demonstrate the degrees of expected congestion, and also the impact of unpredictable events that cause additional traffic in the city [ 10 ]. In this work, we extend our previous contribution by analyzing, modeling and characterizing how traffic becomes distributed along a city, gathering details about the number of vehicles traveling along the different street segments, as well as their travel times [ 11 ], using the reference traffic as input load to the Simulation of Urban MObility (SUMO) tool [ 12 ]. Post-processing of the gathered data allows merging segments when excessive fragmentation is detected, characterizing the different streets in terms of travel time behavior under variable traffic loads.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and Characterization of Traffic Flows in Urban Environments

Zambrano-Martinez

Calafate

Soler

et al. 2018

Sensors

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Currently, one of the main challenges faced in large metropolitan areas is traffic congestion. To address this problem, adequate traffic control could produce many benefits, including reduced pollutant emissions and reduced travel times. If it were possible to characterize the state of traffic by predicting future traffic conditions for optimizing the route of automated vehicles, and if these measures could be taken to preventively mitigate the effects of congestion with its related problems, the overall traffic flow could be improved. This paper performs an experimental study of the traffic distribution in the city of Valencia, Spain, characterizing the different streets of the city in terms of vehicle load with respect to the travel time during rush hour traffic conditions. Experimental results based on realistic vehicular traffic traces from the city of Valencia show that only some street segments fall under the general theory of vehicular flow, offering a good fit using quadratic regression, while a great number of street segments fall under other categories. Although in some cases such discrepancies are related to lack of traffic, injecting additional vehicles shows that significant mismatches still persist. Thus, in this paper we propose an equation to characterize travel times over a segment belonging to the sigmoid family; specifically, we apply logistic regression, being able to significantly improve the curve fitting results for most of the street segments under analysis. Based on our regression results, we performed a clustering analysis of the different street segments, showing that they can be classified into three well-defined categories, which evidences a predictable traffic distribution using the logistic regression throughout the city during rush hours, and allows optimizing the traffic for automated vehicles.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and Characterization of Traffic Flows in Urban Environments

Zambrano-Martinez

Calafate

Soler

et al. 2018

Sensors

Self Cite

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“…As illustrated, we can find thousands of solutions for the same source and the same destination because this is a big road network with 7,976 nodes and 14,742 edges. Therefore, selecting optimal roads in terms of short distance, time and less congested routes is a very challenging task for path planners in large cities like Valencia, Zambrano-Martinez et al . (2017), “which is the third largest metropolitan area in Spain” (Calafate et al ., 2015).…”

Section: Routing Problem In a Road Traffic Systemmentioning

confidence: 99%

Urban traffic flow management on large scale using an improved ACO for a road transportation system

Boubedra,

Tolba,

Manzoni

et al. 2023

IJICC

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PurposeWith the demographic increase, especially in big cities, heavy traffic, traffic congestion, road accidents and augmented pollution levels hamper transportation networks. Finding the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams, optimizing travel time, decreasing fuel consumption and reducing pollution levels accordingly. In this regard, the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives, such as shortness and rapidness.Design/methodology/approachAn improved ant colony algorithm (ACO) is used to calculate the optimal routes in an urban road network by adopting an elitism strategy, a random search approach and a flexible pheromone deposit-evaporate mechanism. In addition, the authors make a trade-off between route length, travel time and congestion level.FindingsExperimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30% in comparison with the ACO algorithm. In addition, the authors maintain a level of accuracy between 0.9 and 0.95. Therefore, the overall cost of the found solutions decreased from 67 to 40. In addition, the experimental results demonstrate that the authors’ improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) in terms of reducing travel costs and improving overall fitness value.Originality/valueThe proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors, such as travel length, time and congestion level, into the route selection process. Furthermore, random search, elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and effective. These enhancements contribute to the originality of the authors’ work, and they have the potential to advance the field of traffic routing.

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“…In a previous work [2] we created realistic traffic models that reliably describe vehicular behavior in the city of Valencia, and the characterization of the traffic conditions of the different streets during rush hours, by predicting the impact of traffic conditions on travel times [3]. We started by using public data associated with induction loop measurements that are made available by the City Hall of Valencia [4].…”

Section: Introductionmentioning

confidence: 99%

A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas

et al. 2019

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Currently, one of the main challenges that large metropolitan areas must face is traffic congestion. To address this problem, it becomes necessary to implement an efficient solution to control traffic that generates benefits for citizens, such as reducing vehicle journey times and, consequently, environmental pollution. By properly analyzing traffic demand, it is possible to predict future traffic conditions, using this information for the optimization of the routes taken by vehicles. Such an approach becomes especially effective if applied in the context of autonomous vehicles, which have a more predictable behavior, thus enabling city management entities to mitigate the effects of traffic congestion and pollution, thereby improving the traffic flow in a city in a fully centralized manner. This paper represents a step forward towards this novel traffic management paradigm by proposing a route server capable of handling all the traffic in a city, and balancing traffic flows by accounting for present and future traffic congestion conditions. We perform a simulation study using real data of traffic congestion in the city of Valencia, Spain, to demonstrate how the traffic flow in a typical day can be improved using our proposed solution. Experimental results show that our proposed traffic prediction equation, combined with frequent updating of traffic conditions on the route server, can achieve substantial improvements in terms of average travel speeds and travel times, both indicators of lower degrees of congestion and improved traffic fluidity.

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Analysis and Classification of the Vehicular Traffic Distribution in an Urban Area

Cited by 7 publications

References 14 publications

Modeling and Characterization of Traffic Flows in Urban Environments

Modeling and Characterization of Traffic Flows in Urban Environments

Urban traffic flow management on large scale using an improved ACO for a road transportation system

A Centralized Route-Management Solution for Autonomous Vehicles in Urban Areas

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