scholarly journals Electrical Vehicle Charging Services Planning and Operation with Interdependent Power Networks and Transportation Networks: A Review of the Current Scenario and Future Trends

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3371 ◽  
Author(s):  
Shafqat Jawad ◽  
Junyong Liu
Keyword(s):  
Power Distribution ◽  
Traffic Congestion ◽  
Large Scale ◽  
Network Performance ◽  
Transportation Networks ◽  
Transportation Systems ◽  
Survey Analysis ◽  
Power Networks ◽  
Interdependent Network ◽  
Electrical Vehicle

The growing trend in electrical vehicle (EV) deployment has transformed independent power network and transportation network studies into highly congested interdependent network performance evaluations assessing their impact on power and transportation systems. Electrified transportation is highly capable of intensifying the interdependent correlations across charging service, transportation, and power networks. However, the evaluation of the complex coupled relationship across charging services, transportation, and power networks poses several challenges, including an impact on charging scheduling, traffic congestion, charging loads on the power grid, and high costs. Therefore, this article presents comparative survey analytics of large-scale EV integration’s impact on charging service network scheduling, transportation networks, and power networks. Moreover, price mechanism strategies to determine the charging fares, minimize investment profits, diminish traffic congestion, and reduce power distribution constraints under the influence of various factors were carried out. Additionally, the survey analysis stipulates the interdependent network performance index, ascertaining travel distance, route selection, long-term and short-term planning, and different infrastructure strategies. Finally, the limitations of the proposed study, potential research trends, and critical technologies are demonstrated for future inquiries.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals Flying Car Transportation System: Advances, Techniques, and Challenges

2020 ◽  
Author(s):  
Gaofeng pan ◽  
Mohamed-Slim Alouini
Keyword(s):  
Public Transportation ◽  
Traffic Congestion ◽  
Large Scale ◽  
Ethical Issues ◽  
Transportation Systems ◽  
Long Distance ◽  
Environmental Friendliness ◽  
Safety Issues ◽  
Inherent Nature ◽  
Car Transportation

In order to fulfill transportation demands, people have well-explored ground, waterborne, and high-altitude spaces (HAS) for transportation purposes, as well as the underground space under cities (namely, subway systems). However, due to the increased burdens of population and urbanization in recent decades, huge pressures on public transportation and freight traffic are introduced to cities, plaguing the governors and constraining the development of economics. By observing the fact that near-ground space (NGS) has rarely been utilized, researchers and practitioners started to re-examine, propose and develop flying cars, which are not a totally novel idea, aiming at solving the traffic congestion problem and releasing the strains of cities. Flying cars completely differ from traditional grounded transportation systems, where automobiles/trains are suffering track limitations and are also different from the air flights in HAS for long-distance transfer. Therefore, while observing the lack of specific literature on flying cars and flying car transportation systems (FCTS), this paper is motivated to study the advances, techniques, and challenges of FCTS imposed by the inherent nature of NGS transportation and to devise useful proposals for facilitating the construction and commercialization of FCTS, as well as to facilitate the readers understanding of the incoming FCTS. We first introduce the increased requirements for transportation and address the advantages of flying cars. Next, a brief overview of the developing history of flying cars is presented in view of both timeline and technique categories. Then, we discuss and compare the state of the art in the design of flying cars, including take-off \& landing (TOL) modes, pilot modes, operation modes, and power types, which are respectively related to the adaptability, flexibility & comfort, stability & complexity, environmental friendliness of flying cars. Additionally, since large-scale operations of flying cars can improve the aforementioned transportation problem, we also introduce the designs of FCTS, including path and trajectory planning, supporting facilities and commercial designs. Finally, we discuss the challenges which might be faced while developing and commercializing FCTS from three aspects: safety issues, commercial issues, and ethical issues.

Multi-modal transportation recommendation with unified route representation learning

2020 ◽  
Vol 14 (3) ◽  
pp. 342-350
Author(s):  
Hao Liu ◽  
Jindong Han ◽  
Yanjie Fu ◽  
Jingbo Zhou ◽  
Xinjiang Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
Transportation Networks ◽  
Representation Learning ◽  
Transportation Systems ◽  
Graph Representation ◽  
Dynamic Graph ◽  
Arbitrary Length ◽  
Task Learning ◽  
Semantic Coherence ◽  
Spatio Temporal

Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.

scholarly journals Large-Scale Traffic Congestion Prediction Based on the Symmetric Extreme Learning Machine Cluster Fast Learning Method

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 730 ◽  
Author(s):  
Yiming Xing ◽  
Xiaojuan Ban ◽  
Xu Liu ◽  
Qing Shen
Keyword(s):  
Extreme Learning Machine ◽  
Traffic Congestion ◽  
Large Scale ◽  
High Efficiency ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Fast Learning ◽  
Congestion Prediction ◽  
Learning Machine ◽  
Scale Data

The prediction of urban traffic congestion has emerged as one of the most pivotal research topics of intelligent transportation systems (ITSs). Currently, different neural networks have been put forward in the field of traffic congestion prediction and have been put to extensive use. Traditional neural network training takes a long time in addition to easily falling into the local optimal and overfitting. Accordingly, this inhibits the large-scale application of traffic prediction. On the basis of the theory of the extreme learning machine (ELM), the current paper puts forward a symmetric-ELM-cluster (S-ELM-Cluster) fast learning methodology. In this suggested methodology, the complex learning issue of large-scale data is transformed into different issues on small- and medium-scale data sets. Additionally, this methodology makes use of the extreme learning machine algorithm for the purpose of training the subprediction model on each different section of road, followed by establishing a congestion prediction model cluster for all the roads in the city. Together, this methodology fully exploits the benefits associated with the ELM algorithm in terms of accuracy over smaller subsets, high training speed, fewer parameters, and easy parallel acceleration for the realization of high-accuracy and high-efficiency large-scale traffic congestion data learning.

scholarly journals Evaluation of Microsimulation Models for Roadway Segments with Different Functional Classifications in Northern Iran

2021 ◽  
Vol 6 (3) ◽  
pp. 46
Author(s):  
Amir Masoud Rahimi ◽  
Maxim A. Dulebenets ◽  
Arash Mazaheri
Keyword(s):  
Intelligent Transportation Systems ◽  
Network Performance ◽  
Traffic Simulation ◽  
Transportation Networks ◽  
Transportation Network ◽  
Travel Speed ◽  
Transportation Systems ◽  
Mitigation Strategies ◽  
Northern Iran ◽  
Microsimulation Models

Industrialization, urban development, and population growth in the last decades caused a significant increase in congestion of transportation networks across the world. Increasing congestion of transportation networks and limitations of the traditional methods in analyzing and evaluating the congestion mitigation strategies led many transportation professionals to the use of traffic simulation techniques. Nowadays, traffic simulation is heavily used in a variety of applications, including the design of transportation facilities, traffic flow management, and intelligent transportation systems. The literature review, conducted as a part of this study, shows that many different traffic simulation packages with various features have been developed to date. The present study specifically focuses on a comprehensive comparative analysis of the advanced interactive microscopic simulator for urban and non-urban networks (AIMSUN) and SimTraffic microsimulation models, which have been widely used in the literature and practice. The evaluation of microsimulation models is performed for the four roadway sections with different functional classifications, which are located in the northern part of Iran. The SimTraffic and AIMSUN microsimulation models are compared in terms of the major transportation network performance indicators. The results from the conducted analysis indicate that AIMSUN returned smaller errors for the vehicle flow, travel speed, and total travel distance. On the other hand, SimTraffic provided more accurate values of the travel time. Both microsimulation models were able to effectively identify traffic bottlenecks. Findings from this study will be useful for the researchers and practitioners, who heavily rely on microsimulation models in transportation planning.

scholarly journals Flying Car Transportation System: Advances, Techniques, and Challenges

2020 ◽  
Author(s):  
Gaofeng pan ◽  
Mohamed-Slim Alouini
Keyword(s):  
Public Transportation ◽  
Traffic Congestion ◽  
Large Scale ◽  
Ethical Issues ◽  
Transportation Systems ◽  
Long Distance ◽  
Environmental Friendliness ◽  
Safety Issues ◽  
Inherent Nature ◽  
Car Transportation

In order to fulfill transportation demands, people have well-explored ground, waterborne, and high-altitude spaces (HAS) for transportation purposes, as well as the underground space under cities (namely, subway systems). However, due to the increased burdens of population and urbanization in recent decades, huge pressures on public transportation and freight traffic are introduced to cities, plaguing the governors and constraining the development of economics. By observing the fact that near-ground space (NGS) has rarely been utilized, researchers and practitioners started to re-examine, propose and develop flying cars, which are not a totally novel idea, aiming at solving the traffic congestion problem and releasing the strains of cities. Flying cars completely differ from traditional grounded transportation systems, where automobiles/trains are suffering track limitations and are also different from the air flights in HAS for long-distance transfer. Therefore, while observing the lack of specific literature on flying cars and flying car transportation systems (FCTS), this paper is motivated to study the advances, techniques, and challenges of FCTS imposed by the inherent nature of NGS transportation and to devise useful proposals for facilitating the construction and commercialization of FCTS, as well as to facilitate the readers understanding of the incoming FCTS. We first introduce the increased requirements for transportation and address the advantages of flying cars. Next, a brief overview of the developing history of flying cars is presented in view of both timeline and technique categories. Then, we discuss and compare the state of the art in the design of flying cars, including take-off \& landing (TOL) modes, pilot modes, operation modes, and power types, which are respectively related to the adaptability, flexibility & comfort, stability & complexity, environmental friendliness of flying cars. Additionally, since large-scale operations of flying cars can improve the aforementioned transportation problem, we also introduce the designs of FCTS, including path and trajectory planning, supporting facilities and commercial designs. Finally, we discuss the challenges which might be faced while developing and commercializing FCTS from three aspects: safety issues, commercial issues, and ethical issues.

A Comprehensive Study on Intelligent Transportation Systems

2018 ◽  
Vol 4 (10) ◽  
pp. 10
Author(s):  
Ankur Mishra ◽  
Aayushi Priya
Keyword(s):  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Transportation Systems ◽  
Environmental Conservation ◽  
Integrated System ◽  
Transport Systems ◽  
Transport Sector ◽  
Intelligent Transport System ◽  
Intelligent Transport ◽  
Transportation Sector

Transportation or transport sector is a legal source to take or carry things from one place to another. With the passage of time, transportation faces many issues like high accidents rate, traffic congestion, traffic & carbon emissions air pollution, etc. In some cases, transportation sector faced alleviating the brutality of crash related injuries in accident. Due to such complexity, researchers integrate virtual technologies with transportation which known as Intelligent Transport System. Intelligent Transport Systems (ITS) provide transport solutions by utilizing state-of-the-art information and telecommunications technologies. It is an integrated system of people, roads and vehicles, designed to significantly contribute to improve road safety, efficiency and comfort, as well as environmental conservation through realization of smoother traffic by relieving traffic congestion. This paper aims to elucidate various aspects of ITS - it's need, the various user applications, technologies utilized and concludes by emphasizing the case study of IBM ITS.

Awareness Analysis of Smart Car Parking System in Heterogeneous High Density Clusters

2017 ◽  
Vol DC CPS 2017 (01) ◽  
pp. 22-26 ◽  
Author(s):  
Elsie Chidinma Anderson ◽  
A. A. Obayi ◽  
K.C. Okafor
Keyword(s):  
Traffic Congestion ◽  
Urban Population ◽  
New Technology ◽  
Innovative Technology ◽  
Survey Analysis ◽  
Metropolitan Cities ◽  
Port Harcourt ◽  
Parking System ◽  
Statistical Survey ◽  
Co Emission

The swiftly growing urban population of Nigeria is generating lots of tension in the cities in line with the rapid increase of vehicles. This is due to hitherto reliance on the present parking system which has no standard to check for parking spaces, hence generating problems such as traffic congestion, time wastage in search of parking slot, fuel consumption/CO emission, insecurity of vehicles etc. This work presents a quantitative statistical survey analysis conducted in selected metropolitan cities in Port Harcourt, Nigeria. The aim is to create awareness on Smart Car Parking System (SCPS) for heterogeneous clustered environments. The results of the conducted analysis showed that the awareness of this innovative technology is still at its tender stage in Nigeria. Findings shows that people are willing to adopt this new technology to assist in overcoming the challenges faced in the present parking system that is unstructured. A brief description of proposed SCPS based on Big data hardware is presented.

scholarly journals HTPC: heterogeneous traffic-aware partition coding for random packet spraying in data center networks

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiawei Huang ◽  
Shiqi Wang ◽  
Shuping Li ◽  
Shaojun Zou ◽  
Jinbin Hu ◽  
...  
Keyword(s):  
Data Center ◽  
Large Scale ◽  
Network Performance ◽  
Rooted Tree ◽  
Heterogeneous Traffic ◽  
Data Center Networks ◽  
Packet Reordering ◽  
Traffic Characteristics ◽  
Network Utilization ◽  
The Impact

AbstractModern data center networks typically adopt multi-rooted tree topologies such leaf-spine and fat-tree to provide high bisection bandwidth. Load balancing is critical to achieve low latency and high throughput. Although the per-packet schemes such as Random Packet Spraying (RPS) can achieve high network utilization and near-optimal tail latency in symmetric topologies, they are prone to cause significant packet reordering and degrade the network performance. Moreover, some coding-based schemes are proposed to alleviate the problem of packet reordering and loss. Unfortunately, these schemes ignore the traffic characteristics of data center network and cannot achieve good network performance. In this paper, we propose a Heterogeneous Traffic-aware Partition Coding named HTPC to eliminate the impact of packet reordering and improve the performance of short and long flows. HTPC smoothly adjusts the number of redundant packets based on the multi-path congestion information and the traffic characteristics so that the tailing probability of short flows and the timeout probability of long flows can be reduced. Through a series of large-scale NS2 simulations, we demonstrate that HTPC reduces average flow completion time by up to 60% compared with the state-of-the-art mechanisms.

Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery

2021 ◽  
Vol 5 (1) ◽  
pp. 1-23
Author(s):  
Yan Pan ◽  
Shining Li ◽  
Qianwu Chen ◽  
Nan Zhang ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Traffic Congestion ◽  
Large Scale ◽  
Load Capacity ◽  
Scheduling Algorithm ◽  
Cost Effective ◽  
Promising Alternative ◽  
Logistics Industry ◽  
Last Mile ◽  
Delivery Scheduling ◽  
Schedule Approach

Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destination, gets recharged by the wireless recharger deployed on the bus, and then flies from the bus to the parcel destination. This novel approach not only increases the delivery range and load capacity of battery-limited UAVs, but is also much more cost-effective and environment-friendly than traditional methods. New challenges therefore emerge as the buses with spatiotemporal mobility become the bottleneck during delivery. By landing on buses, an Energy-Neutral Flight Principle and a delivery scheduling algorithm are proposed for the UAVs. Using the Energy-Neutral Flight Principle, each UAV can plan a flying path without depleting energy given buses with uncertain velocities. Besides, the delivery scheduling algorithm optimizes the delivery time and number of delivered parcels given warehouse location, logistics UAVs, parcel locations and buses. Comprehensive evaluations using a large-scale bus dataset demonstrate the superiority of the innovative logistics UAV schedule approach.

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