Sustainability

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26 pages, 2345 KiB

Open AccessArticle

Methodological Evaluation to Integrate Charging Stations for Electric Vehicles in a Tram System Using OpenDSS—A Case Study in Ecuador

by Marco Toledo-Orozco, Eddy Bravo-Padilla, Carlos Álvarez-Bel, Diego Morales-Jadan and Luis Gonzalez-Morales

Sustainability 2023, 15(8), 6382; https://doi.org/10.3390/su15086382 - 7 Apr 2023

Cited by 4 | Viewed by 2148

Abstract

The difficulties in transitioning to electric mobility in developing countries lie in the lack of charging infrastructure for electric vehicles and buses. This research proposes a novel methodology to integrate electric vehicles and buses to optimise the tramway infrastructure. It is necessary to [...] Read more.

The difficulties in transitioning to electric mobility in developing countries lie in the lack of charging infrastructure for electric vehicles and buses. This research proposes a novel methodology to integrate electric vehicles and buses to optimise the tramway infrastructure. It is necessary to address challenges from the technical point of view by analysing the stochasticity of its variables through simulation in OpenDSS software. The technical feasibility of the tram power system and the impacts caused in the distribution network due to the incorporation of charging stations in three operating scenarios: the first in slow charging, the second in fast charging, and a third scenario that combines the previous two methods. The simulations determine that slow charging at night represents 9% of the total bus fleet, improving the utilisation factor of the tram system from 11% to 32%, whereas the fast and combined charging of vehicles and buses is not feasible due to the increase in losses in the system due to overloading in the network; however, the study validates the penetration of certain charging stations in the tramway network in a real case. Full article

(This article belongs to the Special Issue Advance in Transportation, Smart City, and Sustainability)

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14 pages, 2310 KiB

Open AccessArticle

A Probabilistic Hill-Climbing Algorithm for the Single-Source Transportation Problem

by Pisut Pongchairerks

Sustainability 2023, 15(5), 4289; https://doi.org/10.3390/su15054289 - 28 Feb 2023

Viewed by 1760

Abstract

This paper proposes a probabilistic hill-climbing algorithm, called PH, for the single-source transportation problem (STP). PH is a tree search algorithm in which each node contains an assignment problem (AP) transformed from the STP being solved. The transformation converts each source’s product units [...] Read more.

This paper proposes a probabilistic hill-climbing algorithm, called PH, for the single-source transportation problem (STP). PH is a tree search algorithm in which each node contains an assignment problem (AP) transformed from the STP being solved. The transformation converts each source’s product units into product lots; a product lot equals multiple product units. The AP aims to find the optimal assignment of product lots to destinations to minimize the total assignment cost. PH uses the Hungarian method to find the optimal solution of the AP in every node, which is a solution of the STP. For the AP of the root node (as the initial current node), the number of each source’s product lots is set to be small enough to guarantee the generation of a feasible solution for the STP. To generate every subsequent level, the current node is branched into multiple child nodes, in which the number of child nodes equals the number of sources in the STP. The AP of each child node is modified from the AP of the current node by adding one more product lot into a specific different source. Consequently, each child node provides a solution that is better than or the same as the current node’s solution; however, some child nodes’ solutions may be infeasible for the STP due to the insufficiency of a source’s capacity. If all of the child nodes cannot find a better feasible solution than the current node’s solution, PH stops its procedure. To diversify the search, PH selects one of the child nodes as the new current node in a probabilistic way, instead of always selecting the best child node. The experiment’s results in this paper reveal the performance of the three variants of PH. Full article

(This article belongs to the Special Issue Advance in Transportation, Smart City, and Sustainability)

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13 pages, 750 KiB

Open AccessArticle

Multi-Agent Reinforcement Learning for Traffic Signal Control: A Cooperative Approach

by Máté Kolat, Bálint Kővári, Tamás Bécsi and Szilárd Aradi

Sustainability 2023, 15(4), 3479; https://doi.org/10.3390/su15043479 - 14 Feb 2023

Cited by 13 | Viewed by 4784

Abstract

The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer [...] Read more.

The rapid growth of urbanization and the constant demand for mobility have put a great strain on transportation systems in cities. One of the major challenges in these areas is traffic congestion, particularly at signalized intersections. This problem not only leads to longer travel times for commuters, but also results in a significant increase in local and global emissions. The fixed cycle of traffic lights at these intersections is one of the primary reasons for this issue. To address these challenges, applying reinforcement learning to coordinating traffic light controllers has become a highly researched topic in the field of transportation engineering. This paper focuses on the traffic signal control problem, proposing a solution using a multi-agent deep Q-learning algorithm. This study introduces a novel rewarding concept in the multi-agent environment, as the reward schemes have yet to evolve in the following years with the advancement of techniques. The goal of this study is to manage traffic networks in a more efficient manner, taking into account both sustainability and classic measures. The results of this study indicate that the proposed approach can bring about significant improvements in transportation systems. For instance, the proposed approach can reduce fuel consumption by 11% and average travel time by 13%. The results of this study demonstrate the potential of reinforcement learning in improving the coordination of traffic light controllers and reducing the negative impacts of traffic congestion in urban areas. The implementation of this proposed solution could contribute to a more sustainable and efficient transportation system in the future. Full article

(This article belongs to the Special Issue Advance in Transportation, Smart City, and Sustainability)

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20 pages, 7231 KiB

Open AccessArticle

NA-DGRU: A Dual-GRU Traffic Speed Prediction Model Based on Neighborhood Aggregation and Attention Mechanism

by Xiaoping Tian, Changkuan Zou, Yuqing Zhang, Lei Du and Song Wu

Sustainability 2023, 15(4), 2927; https://doi.org/10.3390/su15042927 - 6 Feb 2023

Cited by 3 | Viewed by 1542

Abstract

Traffic prediction is an important part of the Intelligent Transportation System (ITS) and has broad application prospects. However, traffic data are affected not only by time, but also by the traffic status of other nearby roads. They have complex temporal and spatial correlations. [...] Read more.

Traffic prediction is an important part of the Intelligent Transportation System (ITS) and has broad application prospects. However, traffic data are affected not only by time, but also by the traffic status of other nearby roads. They have complex temporal and spatial correlations. Developing a means for extracting specific features from them and effectively predicting traffic status such as road speed remains a huge challenge. Therefore, in order to reduce the speed prediction error and improve the prediction accuracy, this paper proposes a dual-GRU traffic speed prediction model based on neighborhood aggregation and the attention mechanism: NA-DGRU (Neighborhood aggregation and Attention mechanism–Dual GRU). NA-DGRU uses the neighborhood aggregation method to extract spatial features from the neighborhood space of the road, and it extracts the correlation between speed and time from the original features and neighborhood aggregation features through two GRUs, respectively. Finally, the attention model is introduced to collect and summarize the information of the road and its neighborhood in the global time to perform traffic prediction. In this paper, the prediction performance of NA-DGRU is tested on two real-world datasets, SZ-taxi and Los-loop. In the 15-, 30-, 45- and 60-min speed prediction results of NA-DGRU on the SZ-taxi dataset, the RMSE values were 4.0587, 4.0683, 4.0777 and 4.0851, respectively, and the MAE values were 2.7387, 2.728, 2.7393 and 2.7487; on the Los-loop dataset, the RMSE values for the speed prediction results were 5.1348, 6.1358, 6.7604 and 7.2776, respectively, and the MAE values were 3.0281, 3.6692, 4.0567 and 4.4256, respectively. On the SZ-taxi dataset, compared with other baseline methods, NA-DGRU demonstrated a maximum reduction in RMSE of 6.49% and a maximum reduction in MAE of 6.17%; on the Los-loop dataset, the maximum reduction in RMSE was 31.01%, and the maximum reduction in MAE reached 24.89%. Full article

(This article belongs to the Special Issue Advance in Transportation, Smart City, and Sustainability)

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18 pages, 2700 KiB

Open AccessArticle

Simulation Evaluation of a Current Limiting Scheme in an Urban Rail Transit Network

by Hexin Hu, Jitao Li and Shuai Wu

Sustainability 2023, 15(1), 375; https://doi.org/10.3390/su15010375 - 26 Dec 2022

Cited by 2 | Viewed by 1393

Abstract

The formulation of the current limiting scheme of an urban rail transit network is a complex multi-objective planning problem as the effect of the current limiting scheme is unknown before implementation. In this article, a method combining discrete event simulation and agent simulation [...] Read more.

The formulation of the current limiting scheme of an urban rail transit network is a complex multi-objective planning problem as the effect of the current limiting scheme is unknown before implementation. In this article, a method combining discrete event simulation and agent simulation is used to study the simulation scheduling principle of the current limiting scheme, and a modeling method based on an abstract agent group is proposed. Based on the AnyLogic simulation platform, a meso-scale simulation model for evaluating the current limiting scheme of urban rail transit networks was developed, and a logical framework for the operation simulation of the intelligent group and urban rail network system with stations, passengers, and trains as units was constructed. Furthermore, the data exchanges between stations, trains, and passengers were controlled through discrete events of driving. The results show that the constructed simulation model can effectively replace the actual system to evaluate the current limiting scheme and reduce the computational redundancy of passenger agents flowing in the urban rail network system and the cost of model transformation. Full article

(This article belongs to the Special Issue Advance in Transportation, Smart City, and Sustainability)

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23 pages, 3793 KiB

Open AccessArticle

Joint Optimization of Allocations and Relocations in One-Way Carsharing Systems with Two Operators

by Rongqin Lu

Sustainability 2022, 14(22), 15308; https://doi.org/10.3390/su142215308 - 17 Nov 2022

Cited by 1 | Viewed by 1017

Abstract

Multiple operators commonly coexist in one-way carsharing systems. Therefore, the performance of the system is worth exploring. We used one-way carsharing systems with two operators as an example, assuming that one joins first and is called the leader, and another is named the [...] Read more.

Multiple operators commonly coexist in one-way carsharing systems. Therefore, the performance of the system is worth exploring. We used one-way carsharing systems with two operators as an example, assuming that one joins first and is called the leader, and another is named the follower. A nonlinear mixed-integer bilevel programming model is set to jointly optimize the allocations (including the number of shared cars and parking spaces) and the relocations. The users’ preferences are included by comprehensively considering the travel cost, number of available shared cars at the departing station, and the number of parking spaces at the arrival station. Relocations are also performed in the upper-level model and the lower-level model to maximize the profits of the leader and the follower, respectively. The models of both levels connect by setting the number of parking spaces at each station and the users’ choice between operators. A customized adaptive genetic algorithm is proposed based on the characteristic of the model. Case studies in Beijing reveal that, compared to a single-operator carsharing system, the total profit and demand satisfied by shared cars increased significantly in two-operator carsharing systems, with increases of 37.59% and 56.55%, respectively. Considering the users’ preferences, the leader can meet 266.84% more demands and earn a 174.76% higher profit. As for the follower, the corresponding growth rates are 124.98% and 36.30%, respectively. Full article

(This article belongs to the Special Issue Advance in Transportation, Smart City, and Sustainability)

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14 pages, 533 KiB

Open AccessArticle

A Q-Learning-Based Approximate Solving Algorithm for Vehicular Route Game

by Le Zhang, Lijing Lyu, Shanshui Zheng, Li Ding and Lang Xu

Sustainability 2022, 14(19), 12033; https://doi.org/10.3390/su141912033 - 23 Sep 2022

Cited by 2 | Viewed by 1286

Abstract

Route game is recognized as an effective method to alleviate Braess’ paradox, which generates a new traffic congestion since numerous vehicles obey the same guidance from the selfish route guidance (such as Google Maps). The conventional route games have symmetry since vehicles’ payoffs [...] Read more.

Route game is recognized as an effective method to alleviate Braess’ paradox, which generates a new traffic congestion since numerous vehicles obey the same guidance from the selfish route guidance (such as Google Maps). The conventional route games have symmetry since vehicles’ payoffs depend only on the selected route distribution but not who chose, which leads to the precise Nash equilibrium being able to be solved by constructing a special potential function. However, with the arrival of smart cities, the real-time of route schemes is more of a concerned of engineers than the absolute optimality in real traffic. It is not an easy task to re-construct the new potential functions of the route games due to the dynamic traffic conditions. In this paper, compared with the hard-solvable potential function-based precise method, a matched Q-learning algorithm is designed to generate the approximate Nash equilibrium of the classic route game for real-time traffic. An experimental study shows that the Nash equilibrium coefficients generated by the Q-learning-based approximate solving algorithm all converge to 1.00, and still have the required convergence in the different traffic parameters. Full article

(This article belongs to the Special Issue Advance in Transportation, Smart City, and Sustainability)

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22 pages, 3935 KiB

Open AccessArticle

Investigating the Effect of Network Traffic Signal Timing Strategy with Dynamic Variable Guidance Lanes

by Fei Zhao, Liping Fu, Xiaofeng Pan, Tae J. Kwon and Ming Zhong

Sustainability 2022, 14(15), 9394; https://doi.org/10.3390/su14159394 - 1 Aug 2022

Viewed by 1594

Abstract

This paper aims to investigate the effect of network signal timing strategy with dynamic variable guidance lanes based on a two-step approach, where the first step is an interactive traffic signal optimization model for each single interaction (e.g., lane allocation plans, cycle length) [...] Read more.

This paper aims to investigate the effect of network signal timing strategy with dynamic variable guidance lanes based on a two-step approach, where the first step is an interactive traffic signal optimization model for each single interaction (e.g., lane allocation plans, cycle length) in the network, and the second refers to network signal control (e.g., split, off-sets). The optimization problem in the first step is solved using the Non-dominated Sorting Genetic Algorithm (NSGA-ΙΙ), and the network signal control problem in the second step is solved through SYNCHRO. To verify the effect of dynamic variable guidance lanes and also the reliability and validity of the proposed approach, a numerical case study is carried out. The results show that the average vehicle delay in the entire road network was reduced by 25.06% after optimization using the proposed model. Moreover, the sensitivity of influencing factors of the proposed model is also analyzed. The results show that when the traffic flow is increased by 60% of the original traffic flow, the optimization effect of the model is more significant. However, when the lane capacity is more than 1300 pcu/h, the vehicle delay will increase slowly. To sum up, this method can improve the regional traffic efficiency of the traffic-stressed lanes and further promote the full utilization of space-time resources of the road network. Full article

(This article belongs to the Special Issue Advance in Transportation, Smart City, and Sustainability)

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