Traffic Congestion Problems Research Articles

Personal Car Pooling Using Machine Learning

The ever-increasing growth of cities has implied longer commuting distances (travel times) for their population and traffic congestion problems affecting public transport systems. These factors motivate the use of private vehicles causing an increase on traffic, longer idle times, reduced vehicle capacity utilization, higher mobility costs and a significant increase on vehicle emissions, one of today’s major environmental concerns. By having more people using one vehicle, carpooling reduces each person's travel costs such as fuel costs, tolls and the stress of driving. The datasets are processed using Dijkstra algorithm, KNN algorithm.

Comprehensive service quality evaluation of public transit based on extension cloud model

Prioritizing the development of public transit and enhancing its attractiveness is an important way to solve the problem of urban traffic congestion and achieve sustainable development. To improve the service quality and overall opera-tional efficiency of urban public transit, an evaluation index system related to the comprehensive experience of pas-sengers, service supply quality of public transit enterprises, and supervision of management departments was intro-duced from both the demand and the supply of public transit travel services. Based on the data distribution characteris-tics of the boxplot in statistics, the evaluation level and corresponding value range of each index were determined, and the comprehensive weight of the index was determined using the linear weighting method combining the analytic hierarchy process and the entropy weight method, so as to reduce the influence of single weighting method on the evaluation results of comprehensive service quality of public transit. An evaluation method of public transit compre-hensive service quality based on the extension cloud model was established. The evaluation results of the model were obtained by calculating the cloud affiliation and comprehensive certainty, and a reliability factor was used to test the evaluation results, which solved the problem of randomness and fuzziness in the process of comprehensive service quality evaluation of public transportation and made the evaluation results closer to the reality. Finally, the estab-lished comprehensive evaluation model was applied to a city for example analysis, and the corresponding evaluation level was obtained as good. The value of the reliability factor in the model was less than 0.01, indicating that the model has good applicability and a certain application value for the comprehensive service quality evaluation of public transit. The evaluation method fully considered a variety of evaluation indicators, specified the evaluation level of comprehensive service quality of public transit, and the evaluation results provide a theoretical basis for public transport enterprise to make targeted improvement measures.

Deep Learning Methods in Short-Term Traffic Prediction: A Survey

Nowadays, traffic congestion has become a serious problem that plagues the development of many cities aroundthe world and the travel and life of urban residents. Compared with the costly and long implementation cyclemeasures such as the promotion of public transportation construction, vehicle restriction, road reconstruction, etc., traffic prediction is the lowest cost and best means to solve traffic congestion. Relevant departmentscan give early warnings on congested road sections based on the results of traffic prediction, rationalize thedistribution of police forces, and solve the traffic congestion problem. At the same time, due to the increasingreal-time requirements of current traffic prediction, short-term traffic prediction has become a subject of widespread concern and research. Currently, the most widely used model for short-term traffic prediction are deeplearning models. This survey studied the relevant literature on the use of deep learning models to solve shortterm traffic prediction problem in the top journals of transportation in recent years, summarized the currentcommonly used traffic datasets, the mainstream deep learning models and their applications in this field. Finally, the challenges and future development trends of deep learning models applied in this field are discussed.

TRAFFIC FLOW ANALYSIS AT UNSIGNAL JUNCTION (CASE STUDY: INTERSECTION OF JL. MANDALA – JL. RAYA TLANAKAN PAMEKASAN CITY)

We often find traffic problems in big cities, and even small cities such as Pamekasan. The problem of congestion and traffic congestion often occurs at crossroads, especially the intersection of Jl. Mandala – Jl. Raya Tlanakan, Pamekasan City. The reduced performance of the intersection will cause losses due to a decrease in speed, an increase in delays and vehicle queues and a decrease in environmental quality. Parameters of an unsignalized intersection include capacity, degree of saturation, delay and queuing opportunity. From the analysis, the maximum C (capacity) value of 2257.83 smp/hour does not exceed the basic capacity (Co = 2700 smp/hour). The low capacity classification occurring on Thursday, December 9, 2021 at 18.00 – 21.00 WIB. The highest DS (Degree of Saturation) value is 0.63, and the largest D (Intersection Delay) value is 19.17 sec/smp occurred on Thursday, December 9, 2021 at 06.00 – 10.00 WIB. Then the capacity obtained at the intersection of Jl. Mandala – Jl. Raya Tlanakan, Pamekasan City is still feasible to accommodate the traffic volume because it does not exceed the basic capacity of the intersection. However, to overcome congestion at intersections, especially during rush hour/working hours, it is necessary to supervise and secure from related parties.

Analysis of Driver Behavior and Intervehicular Collision: A Data-Based Traffic Modeling and Simulation Approach

The emergence of intelligent connected vehicles (ICVs) is expected to contribute to resolving traffic congestion and safety problems; however, it is inevitable that ICV safety issues in mixed traffic (involving ICVs and human driven vehicles) will be a critical challenge. The numerical simulation of scenarios involving a mix of different driving profiles is expected to be an important safety assessment tool in the process of testing and validating ICVs, especially regarding extreme scenarios, including car collisions, which are rarely captured in real-world datasets. In this study, we propose a novel approach for car collision generation in numerical simulations based on the assumption that car collision occurrences are mostly associated with certain specific driver profiles. Using a dataset provided by the Next Generation Simulation (NGSIM) project, NGSIM 101 dataset, we identify three different driver profiles: aggressive, inattentive, and normal drivers. We then replicate car collision occurrences by varying the percentages of these three driver profiles in the simulated environment, allowing us to establish a relationship between driver profiles and car collision occurrences. We also investigate the severity of car collisions and classify them with respect to the driver profiles of the cars involved in the collisions. Our approach of replicating car collision occurrences in numerical simulations will facilitate the testing and validation of ICVs in the future, especially regarding the testing of ICV functionalities in dealing with traffic accidents.

Review of Strategies for Curbing Traffic Congestion in Sub-Saharan Africa Cities: Technical and Policy Perspectives

This paper summarizes the causes of traffic congestion in sub-Saharan Africa (SSA) cities and the solutions to be adopted. According to the literature, the modal share for private cars, public transport (minibus and taxis); and walking & cycling are 8%, 34% and 40% respectively. From these figures, it has been found that although the private car mode is still used relatively little (as compared to developed countries); contrary to the expectation, traffic congestion in SSA cities is worse compared to cities of developed countries. This implies that, traffic congestion problem in SSA cities is largely associated with limited availability of road infrastructure. As opposed to traffic congestion solutions to most of the cities in developed countries, road expansion is still one of the major solutions towards solving the same problem in SSA cities. However, the issue remains as to how many lane-kilometers should be added to reach the optimal level. It is clear that we cannot keep expanding roads without a limit; therefore, transportation experts and land-use planners need to establish the benchmarks that will serve as limiting indicators. Apart from relatively poor land-use planning and inadequacy of road infrastructure, it has also been found that disorganized and informal public transport is another major cause of traffic congestion in SSA cities.

Visualizing Impact of Weather on Traffic Congestion Prediction: A Quantitative Study

A substantial amount of research has been done to develop improved Intelligent Transportation Systems (ITS) to alleviate traffic congestion problems. These include methods that incorporate the indirect impact on traffic flow such as weather. In this paper, we studied the impact of weather conditions on traffic congestion along with more spatial and temporal factors, such as weekdays/time and location, which is a different approach to this problem. The proposed solution uses all these indicators to estimate the flow of traffic. We evaluate the level of congestion (LOC) based on the traffic volume grouped in certain regions of the city. The index for the defined LOC indicates the traffic flow from “free -flowing” to “traffic jam”. The data for the traffic volume count is collected from the Department of Transportation (DOT) for NYMTC. Weather conditions along with special and temporal information have an essential role in predicting the congestion level. We used supervised machine learning for this purpose. The prediction models are based on certain factors such as the volume count of the traffic at the entry and exit point of each street pair, particular days of the week, timestamp, geographical location, and weather parameters. The study is done on the major roadways of each of the four prominent boroughs in New York. The results of the traffic prediction model were established by using the Gradient Boosting Regression Tree (GBRT) which showed an accuracy of 97.12%. Moreover, the calculation speed was relatively fast, and it has stronger applicability to the prediction of congestion conditions.

Simulation Based-MCDM Approach for Evaluating Traffic Solutions

Traffic congestion problems have dramatically esca-lated with the increasing volume of vehicles, pedestrians, and cyclists in the face of limited road capacity. This re-search aims to reduce the time road users spend in the system (school-zone area) and improve the efficiency of the process of dropping off and collecting children from a crowded school area. The study integrates discrete-event simulation (DES) and multi-criterion decision-mak-ing (MCDM) techniques to comprehensively evaluate the proposed alternatives to select an optimal solution based on many performance measures. A real-world case study of the traffic and congestion problems experienced by parents when they drop off and fetch their children from school during peak hours is presented. A heuristic algorithm was developed to simulate the random and un-predictable behaviour of road users. A cost-benefit anal-ysis considered the impact of waiting time, traffic den-sity, number of accidents, additional fuel expenses, and emission reduction. The technique for order of preference by similarity to ideal solution (TOPSIS) and preference selection index (PSI) methods were utilised to select the most appropriate option for parents. The study found that the integration of simulation techniques with MCDM methods could efficiently solve traffic problems.

A Comprehensive and Effective Framework for Traffic Congestion Problem Based on the Integration of IoT and Data Analytics

Traffic congestion is still a challenge faced by most countries of the world. However, it can be solved most effectively by integrating modern technologies such as Internet of Things (IoT), fog computing, cloud computing, data analytics, and so on, into a framework that exploits the strengths of these technologies to address specific problems faced in traffic management. Unfortunately, no such framework that addresses the reliability, flexibility, and efficiency issues of smart-traffic management exists. Therefore, this paper proposes a comprehensive framework to achieve a reliable, flexible, and efficient solution for the problem of traffic congestion. The proposed framework has four layers. The first layer, namely, the sensing layer, uses multiple data sources to ensure a reliable and accurate measurement of the traffic status of the streets, and forwards these data to the second layer. The second layer, namely, the fog layer, consumes these data to make efficient decisions and also forwards them to the third layer. The third layer, the cloud layer, permanently stores these data for analytics and knowledge discoveries. Finally, the fourth layer, the services layer, provides assistant services for traffic management. We also discuss the functional model of the framework and the technologies that can be used at each level of the model. We propose a smart-traffic light algorithm at level 1 for the efficient management of congestion at intersections, tweet-classification and image-processing algorithms at level 2 for reliable and accurate decision-making, and support services at level 4 of the functional model. We also evaluated the proposed smart-traffic light algorithm for its efficiency, and the tweet classification and image-processing algorithms for their accuracy.

The One E-Ticket Customized Bus Service Mode for Passengers with Multiple Trips and the Routing Problem

To alleviate the problems of traffic congestion, excessive energy consumption, and the environmental pollution caused by private cars, it is essential to use public transportation (PT). However, passengers making multiple trips in a short time period must repeatedly make travel mode choices, purchase tickets, and wait for buses for each trip, which may negatively affect their preference for PT. In order to improve the attractiveness of PT, especially for passengers requiring multiple trips in a short time period, this paper proposes the one e-ticket customized bus service mode for passengers with multiple trips (OECBSM-PMT) by customized buses (CBs). Besides, a CB-routing optimization model for the OECBSM-PMT is also developed in this paper, formulated as a mixed-integer linear programming based on a vehicle routing problem with pickup and delivery and time windows (VRPPDTW). The model aims to maximize the profit and minimize the costs of operation with considering passengers with multi-trip requests, homogeneous CB fleets with pickup/delivery-time-window constraints, and mixed loads. A service effectiveness identification procedure based on genetic algorithm (GA) is proposed to cope with the calculation considering the characteristics of passengers with multiple trips. Finally, the proposed model and algorithm are verified and analyzed using the case of the 2022 Beijing Winter Olympic Games. It can be found from the results that the method can provide an optimized CB route plan and timetable, and the algorithm GA-I obtains better solutions than other solving strategies in most instances. The proposed OECBSM-PMT and corresponding optimized method can better adapt to diverse travel demands, significantly improve the convenience for passengers, especially those making multiple trips in a short time period and will eventually promote a higher level of public transport service.

Intelligent transportation systems to mitigate road traffic congestion

Intelligent transport systems have efficiently and effectively proved themselves in settling up the problem of traffic congestion around the world. The multi-agent based transportation system is one of the most important intelligent transport systems, which represents an interaction among the neighbouring vehicles, drivers, roads, infrastructure and vehicles. In this paper, two traffic management models have been created to mitigate congestion and to ensure that emergency vehicles arrive as quickly as possible. A tool-chain SUMO-JADE is employed to create a microscopic simulation symbolizing the interactions of traffic. The simulation model has showed a significant reduction of at least 50% in the average time delay and thus a real improvement in the entire journey time.

Identification Method of Main Road Traffic Congestion Situation in Cold-Climate Cities Based on Potential Energy Theory and GPS Data

Traffic congestion is a global problem. Affected by climate, the issue of congestion in cold-climate cities is more serious. To comprehensively and accurately identify the traffic congestion situation on the main roads of cold-climate cities and to provide a reference for city managers for congestion treatment, this study applies the theory of potential energy to the problem of traffic congestion, draws on the symmetry of potential energy and the function mechanism of artificial potential fields, and establishes a traffic congestion potential energy model for the main roads in cold-climate cities. Taking Global Positioning System (GPS) data as the primary data, the model parameters are calibrated using a combination of subjective and objective empowerment methods, and the investigation into the congestion perception level determines the division threshold of the congestion potential energy level. Test results are encouraging, and the method considers the state and the trends and can avoid problems such as lagging road condition information.

Network Design Algorithm Implementation for Resilient Transportation System under Continuous Risk Perturbation with Big Data Analysis

With the rapid economic development and urbanization process accelerating, motor vehicle ownership in large cities is increasing year by year; urban traffic congestion, parking difficulties, and other problems are becoming increasingly serious; in ordinary daily life, continuous risk of disturbance, having a flexible transportation system network is more able to alleviate daily congestion in the city, and the main thing about flexible transportation network is its algorithm. It is worth noting that congestion in many cities is generally reflected in the main roads, while many secondary roads and branch roads are underutilized, and the limited road resources in cities are not fully utilized. As an economic and effective road traffic management measure, one-way traffic can balance the spatial and temporal distribution of traffic pressure within the road network, make full use of the existing urban road network capacity, and solve the traffic congestion problem. Therefore, it is of great theoretical and practical significance to develop a reasonable and scientific one-way traffic scheme according to the characteristics of traffic operation in different regions. Based on the fixed demand model, the influence of traffic demand changes is further considered, the lower-level model is designed as an elastic demand traffic distribution model, the excess demand method is used to transform the elastic demand problem into an equivalent fixed demand problem based on the extended network, and the artificial bee colony algorithm based on risk perturbation is designed to solve the two-level planning model. The case study gives a one-way traffic organization optimization scheme that integrates three factors, namely, the average load degree overload limit of arterial roads, the detour coefficient, and the number of on-street parking spaces on feeder roads, and performs sensitivity analysis on the demand scaling factor.

Measure the ability of cities to be biked via weighted parameters, using GIS tools. The case study of Zografou in Greece

During the past decades, unlike other countries of the European community, Greek society excluded bicycle from its everyday life, both as a solution for sports and recreation, as well as a transport mode. However, in the last few years, maybe due to the economic crisis, people in Greece began to consider cycling, again, in their daily routine. In this context, the present paper endeavours to examine and evaluate the ability to cycle in the complex urban environment of Greek cities. Therefore, this study develops a method for assessing bikeability, based on international literature and related walkability evaluation methods. Initially, a literature review was conducted to identify those parameters that positively affect the suitability of a cycling route. Afterwards, we developed linear scales to transform the quantitative data into scores ranging from 0 to 10, in order to be comparable. In the next step we used participative Analytic Hierarchy Process (AHP) in order to weight each parameter and identify its significance. Lastly, we formulated the final bikeability index. The chosen case study is the Municipality of Zografou, which constitutes one of the most significant municipalities in Athens, combining all aspects of a modern city: a liveable central area, university campuses, high residential density and traffic congestion problems. The results indicate that the overall bikeabilty index of Zografou is quite low, specifically it equals to 3,7/10 meaning that most of the road sections are not suitable or attractive for cycling. This paper provides fruitful insights about the issue of bikeability in urban areas and mostly its spatial dimension and it could be used as decision making tool for stakeholders and planners.

Retracted] Intelligent Transportation Design Based on Iterative Learning

Most of the existing traffic optimization control methods are based on accurate mathematical models. As an uncertain and complex system, the urban traffic system faces difficulty in accurately calibrating the model parameters. Therefore, the existing methods become very difficult in the actual application process. Based on the massive data contained in the urban traffic system and the repetitive characteristics of traffic flow, this paper proposes a hierarchical traffic signal control method for urban road network based on iterative learning control. The simulation results show that the algorithm can achieve better control effect and can solve the problem of urban traffic congestion more effectively than traditional traffic control methods.

Extrinsic Information Aided Fingerprint Localization of Vehicles for Cell-Free Massive MIMO-OFDM System

In smart city, traffic congestion and parking problems will be solved assisted by precise vehicle location. To address the vehicle localization problem in smart city, a novel extrinsic information aided fingerprint localization algorithm is proposed in this paper. Firstly, on the basis of massive multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM), the angle-delay domain channel matrix (ADDCM) is extracted. Then, an amplitude ratio based non-line of sight (NLoS) identification method is provided to estimate link states. For the case of LoS existence, in order to save storage space, a tuple fingerprint is proposed to record angle of LoS path. In other case, when all APs service in NLoS scenarios, to improve the localization robustness in dynamic environments, the correlated ADDCM (CADDCM) is taken as location fingerprint which can extract the constant information related to fixed scattering clusters. A greedy-based fingerprint matching scheme is used to search the nearest reference point (RP). Furthermore, the extrinsic information provided by neighbor vehicles, such as estimated location and measured distance, is utilized to improve the localization stability. Simulation results show that the extrinsic information aided algorithm could improves the localization accuracy and robustness in dynamic environments.

Where Am I Parking: Incentive Online Parking-Space Sharing Mechanism With Privacy Protection

Sharing private parking spaces during their idle time periods has shown great potential for addressing urban traffic congestion and illegitimate parking problems in smart cities. In this article, aiming to address the online parking-space sharing issue while ensuring the privacy of customer parking destination locations, we propose a novel destination privacy-preserving online parking sharing (DPOPS) incentive scheme. In particular, the online parking-space sharing problem is formalized as a social welfare maximization problem in a two-sided market, where parking-space providers (PSPs) and customers are regarded as sellers and buyers. Then, novel threshold value-based rules are designed to determine winners, payments, and reimbursement. Finally, winners are matched by solving a mixed-integer nonlinear programming problem, aiming to minimize the distance between customer’s destination and allocated parking space. In addition, the location privacy of the customers’ destinations is protected by the Laplace mechanism. We prove that DPOPS achieves several economically effective properties and approximate differential privacy. We analyze the upper bound of the efficiency loss of our scheme. Extensive evaluation results demonstrate that our scheme can not only achieve good performance regarding social welfare, PSP satisfaction ratio, privacy preservation, and computation overhead but also leads to shorter travel distances for customers comparing to the baseline scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this article, we address the online parking-space sharing issue with considering the parking-space providers (PSPs) and customers’ individual utility while preserving the location privacy of customers’ destinations. Most of the previous works focused on designing a centralized mechanism for allocating parking spaces without considering the protection of the customers’ location privacy. In particular, we propose an online parking-space sharing scheme called DPOPS, including a novel threshold value-based winner determination rule and a parking-space allocation rule. The proposed scheme DPOPS allows the PSPs and customers submit their bids and asks according to their own willingness and is able to improve the utilization of private parking spaces during their idle time periods. Moreover, the location privacy of customers’ destinations is protected by the Laplace mechanism. The experiments demonstrate that the proposed approach outperforms the exponential-based scheme in terms of PSP satisfaction ratio and the travel distance for parking-space customer. The proposed scheme is helpful in managing the vacant parking space in a competitive market and can be readily implemented in the real-world online parking-space sharing systems.

Collaborative optimization of truck appointment system and yard cranes allocation based on ship time window

With the rapid growth of the global container trade volume and the trend of large-scale container ships. The collection and distribution port efficiency and capacity of container terminals in various countries are facing huge challenges. Due to the randomness of most of the trucks entering the port, container terminals often face the problem of traffic congestion caused by the centralized arrival of trucks in the port and the load operation of the yard operation equipment. Therefore, terminal operators urgently need to manage and optimize all aspects of terminal collection and distribution operations. This paper proposes a truck appointment system that simultaneously considers the time window of the ships and the allocation plan of the yard crans, so as to reasonably allocate the arrival time of each truck and the amount of appointment quota. By means of shortening the time window of each ship’s and rationally allocating the yard cranes operations in the blocks, human and material resources can be fully utilized, so as to alleviate the traffic pressure in the container terminal, shorten the waiting time of ships and trucks, and reduce the cost of terminal operators.

Intelligent Traffic Signal Automation Based on Computer Vision Techniques Using Deep Learning

Traffic congestion in highly populated urban areas is a huge problem these days. A lot of researchers have proposed many systems to monitor traffic flow and handle congestion through different techniques. But the current systems are not reliable enough to perceive traffic signals in real-time. Therefore, we aim to build a system that can efficiently perform real-time environments to solve the traffic congestion problem through signal automation. Since vehicle detection and counting are crucial in any traffic system, we use state-of-the-art deep learning techniques to detect and count vehicles in real-time. We then automate the signal timings by comparing the count of traffic on all sides of a junction. These automated signal timings sufficiently reduce congestion and improve traffic flow. We prepared a dataset of 4500 images and achieved about 91% accuracy by training it on Faster RCNN.

Design and implementation of a safety algorithm on V2V routing protocol

Abstract Vehicle to vehicle (V2V) communications have been widely employed to ameliorate a variety of transportation-related issues in cities, the most significant of which being traffic congestion. The inefficient utilization of resources in vehicle infrastructure is frequently the source of a great deal of traffic congestion. Furthermore, due to its high dynamic nature, incorporating traffic congestion into vehicular traffic decision-making is difficult. In this paper, V2V communications will be implemented as an intelligent transportation systems (ITS) safety mechanism solution to solve these traffic congestion problems. The proposed mechanism is applied over integration between AODV (Ad hoc On-Demand Distance Vector) as one of the best topology-based routing protocols with one of geographic-based routing protocols to overcome OADV limitations. As AODV floods the network with control messages to find a path to the target, and the network is going to host a high volume of control packets. So the proposed method will use Global Positioning System (GPS) coordinates to search the region where a path to the target is likely to be found. As this paper describes the efforts to create a hybrid model that combines a typical reactive routing protocol with a geographic one, there will be two steps to identify the proposed ITS network. In first Phase: Choose the most functioning Geographic routing protocol on the V2V simulation environment. In the second Phase: Integrate this suggested Geographic routing protocol with ADOV protocol to implement ITS architectures. The RIVERBED modulator and NS2 simulator will be utilized to choose the best routing protocol. Based on the paper’s key performance indicators simulation results, Distance-Vector-Based Recovery-Strategy (PBR-DV) geographic routing protocol is selected to integrate with AODV routing protocol. Comparing the proposed method with the independent use of AODV, it demonstrated the effectiveness of the proposed method in overcoming all the problems related to resource scarcity and geographical obstacles.