Optimizing Freight Vehicle Routing in Dynamic Time-Varying Networks with Carbon Dioxide Emission Trajectory Analysis

Song, Rui; Qin, Wanen; Shi, Wen; Xue, Xingjian

doi:10.3390/su152115504

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…• Its calibration and validation using data from the National Cooperative Highway Research Program[39]. Additionally, previous studies have validated CMEM's estimates, confirming it as a widely accepted model capable of generating verifiable emissions estimates[21,[40][41][42][43][44][45]. Consequently, no additional calibration or validation efforts were necessary for this study; •…”

supporting

confidence: 55%

Environmental Assessment of Incorrect Automated Pedestrian Detection and Common Pedestrian Timing Treatments at Signalized Intersections

Gavric,

Erdagi,

Stevanovic

2024

Sustainability

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Existing research has primarily focused on the accuracy of automated pedestrian detection systems, overlooking the consequential environmental impacts arising from false or missed pedestrian detections. To fill these research gaps, this study investigates the emissions and fuel consumption resulting from incorrect pedestrian detection at signalized intersections in microsimulation. To carry out experiments, the authors employ Vissim microsimulation software and the Comprehensive Modal Emission Model (CMEM). For the first time in the literature, missed and false calls are modeled in microsimulation and their environmental impacts are accurately measured. The research highlights the limitations of current automated pedestrian (video) detection systems (APVDSs) technologies in reducing emissions and fuel consumption effectively. While APVDSs offer potential benefits for traffic management, their inability to accurately detect pedestrians undermines their environmental efficacy. This study emphasizes the importance of considering environmental impacts of APVDSs, and challenges the belief that pedestrian recall treatment is the least eco-friendly. Also, the study showed that coupling APVDS or push-button treatments with pedestrian recycle features increases fuel consumption and CO2 by 10% at the intersections with higher pedestrian demand. By understanding the emissions and fuel consumption associated with incorrect detections, transportation agencies can make more informed decisions regarding the implementation and improvement of APVDS technologies.

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supporting

confidence: 55%

Environmental Assessment of Incorrect Automated Pedestrian Detection and Common Pedestrian Timing Treatments at Signalized Intersections

Gavric,

Erdagi,

Stevanovic

2024

Sustainability

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“…The unmanned vehicle delivery routing problem is fundamentally a VRP, which has found a growing range of applications that require its solving in daily life, such as material distributions [9], drone path optimization [10,11], home healthcare system applications [12], public transit services' mode and route optimization [13], electric vehicle path problems considering battery charging status [14][15][16], dynamic VRPs [17,18], emergency material dispatch optimization [19][20][21], service-oriented collaborative VRPs [22], and green delivery path problems [23,24]. In contrast to the traditional vehicle routing problem (VRP), the incorporation of unmanned vehicles facilitates continuous 24 h back-and-forth deliveries without the requirement of human drivers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Among these, GAs stand out due to their advantages, including their global search function, parallel search function, and broad applicability. Consequently, many scholars across diverse fields extensively use genetic algorithms [1,13,18,24,38,47]. In alignment with this trend, this paper adopts a heuristic algorithm for optimization and refines its approach based on a genetic algorithm to solve this optimization model.…”

Section: Igamentioning

confidence: 99%

Consideration of Carbon Emissions in Multi-Trip Delivery Optimization of Unmanned Vehicles

Gao,

Liu,

Wang

et al. 2024

Sustainability

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In order to achieve the goal of low-carbon, efficient delivery using unmanned vehicles, a multi-objective optimization model considering carbon emissions in the problem of optimizing multi-route delivery for unmanned vehicles is proposed. An improved genetic algorithm (IGA) is designed for solving this problem. This study takes into account constraints such as the maximum service duration for delivery, the number of vehicles, and the approved loading capacity of the vehicles, with the objective of minimizing the startup cost, transportation cost, fuel cost, and environmental cost in terms of the carbon dioxide emissions of unmanned vehicles. A combination encoding method based on the integer of the number of trips, the number of vehicles, and the number of customers is used. The inclusion of a simulated annealing algorithm and an elite selection strategy in the design of the IGA enhances the quality and efficiency of the algorithm. The international dataset Solomon RC 208 is used to verify the effectiveness of the model and the algorithm in small-, medium-, and large-scale cases by comparing them with the genetic algorithm (GA) and simulated annealing algorithm (SA). The research results show that the proposed model is applicable to the problem of optimizing the multi-route delivery of unmanned vehicles while considering carbon emissions. Compared with the GA and SA, the IGA demonstrates faster convergence speed and higher optimization efficiency. Additionally, as the problem’s scale increases, the average total cost deviation rate changes significantly, and better delivery solutions for unmanned vehicles are obtained with the IGA. Furthermore, the selection of delivery routes for unmanned vehicles primarily depends on their startup costs and transportation distance, and the choice of different vehicle types has an impact on delivery duration, total distance, and the average number of trips. The delivery strategy that considers carbon emissions shows a 22.6% difference in its total cost compared to the strategy that does not consider carbon emissions. The model and algorithms proposed in this study provide optimization solutions for achieving low-carbon and efficient delivery using unmanned vehicles, aiming to reduce their environmental impact and costs. They also contribute to the development and application of unmanned vehicle technology in the delivery field.

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Evaluating Google Maps’ Eco-Routes: A Metaheuristic-Driven Microsimulation Approach

Jovanovic,

Gavric,

Stevanovic

2024

Geographies

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Eco-routing, as a key strategy for mitigating urban pollution, is gaining prominence due to the fact that minimizing travel time alone does not necessarily result in the lowest fuel consumption. This research focuses on the challenge of selecting environmentally friendly routes within an urban street network. Employing microsimulation modelling and a computer-generated mirror of a small traffic network, the study integrates real-world traffic patterns to enhance accuracy. The route selection process is informed by fuel consumption and emissions data from trajectory parameters obtained during simulation, utilizing the Comprehensive Modal Emission Model (CMEM) for emission estimation. A comprehensive analysis of specific origin–destination pairs was conducted to assess the methodology, with all vehicles adhering to routes recommended by Google Maps. The findings reveal a noteworthy disparity between microsimulation results and Google Maps recommendations for eco-friendly routes within the University of Pittsburgh Campus street network. This incongruence underscores the necessity for further investigations to validate the accuracy of Google Maps’ eco-route suggestions in urban settings. As urban areas increasingly grapple with pollution challenges, such research becomes pivotal for refining and optimizing eco-routing strategies to effectively contribute to sustainable urban mobility.

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Optimizing Freight Vehicle Routing in Dynamic Time-Varying Networks with Carbon Dioxide Emission Trajectory Analysis

Cited by 3 publications

References 36 publications

Environmental Assessment of Incorrect Automated Pedestrian Detection and Common Pedestrian Timing Treatments at Signalized Intersections

Environmental Assessment of Incorrect Automated Pedestrian Detection and Common Pedestrian Timing Treatments at Signalized Intersections

Consideration of Carbon Emissions in Multi-Trip Delivery Optimization of Unmanned Vehicles

Evaluating Google Maps’ Eco-Routes: A Metaheuristic-Driven Microsimulation Approach

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