Road Traffic Congestion Research Articles

RETRACTED ARTICLE: Digital forensics system based on dynamic path prediction and the competitiveness of cross-border e-commerce SMEs

With the continuous increase in the number of urban cars, the problem of vehicle road traffic congestion has become more and more serious. The traffic path dynamic system plays a vital role in alleviating road traffic congestion. By predicting the traffic flow of different sections of the city, the route can be optimized, so as to optimize the traffic route plan and effectively solve the traffic congestion problem. In order to accurately predict the traffic dynamics in each part, this article mainly studies the traffic flow forecasting technology and route dynamic selection technology from three aspects: basic principles, research status at home and abroad, and practical applications. Due to the rapid development of communication technology, smart mobile devices have become one of the mainstream communication tools in today’s society. It is gradually replacing computers as an important tool for information processing, storage, and dissemination. Therefore, the rapid development of forensic technology using mobile digital has become a new hot research issue. In the past 2 years, with the rapid development of the digital and information age, the development of cross-border e-commerce businesses based on the Internet has also shown an unstoppable trend. At present, China’s foreign trade is not optimistic. Cross-border e-commerce uses the Internet to break the limitation of information asymmetry between merchants and customers. Compared with traditional foreign trade, it has the advantages of low cost, high profit, and short order cycle. It is a small- and medium-sized enterprise in China. The development of international trade provides convenience and enhances the competitiveness of enterprises.

Advances in lane marking detection algorithms for all-weather conditions

Driving vehicles in all-weather conditions is challenging as the lane markers tend to be unclear to the drivers for detecting the lanes. Moreover, the vehicles will move slower hence increasing the road traffic congestion which causes difficulties in detecting the lane markers especially for advanced driving assistance systems (ADAS). Therefore, this paper conducts a thorough review on vision-based lane marking detection algorithms developed for all-weather conditions. The review methodology consists of two major areas, which are a review on the general system models employed in the lane marking detection algorithms and a review on the types of weather conditions considered for the algorithms. Throughout the review process, it is observed that the lane marking detection algorithms in literature have mostly considered weather conditions such as fog, rain, haze and snow. A new contour-angle method has also been proposed for lane marker detection. Most of the research work focus on lane detection, but the classification of the types of lane markers remains a significant research gap that is worth to be addressed for ADAS and intelligent transport systems.

Does Road Traffic Congestion Increase Fuel Consumption of Households in Kathmandu City?

The growth of vehicle and road traffic congestion is characteristics of urbanization and urban city and indicators of urban life in developing countries. In Nepal, non-economic factors and non-state factors have accelerated unexpectedly and haphazardly urbanization process, although the country was reengineered into seven provincial federal structure. In this backdrop, this paper empirically examines the growth of traffic congestion and its impact on urban households and livelihood based on 160 vehicle owners and users’ survey at six major traffic routes of two urban cities by applying mixed analytical methods (qualitative cum quantitative), descriptive statistics and multiple regression model. The descriptive statistics result of the study reveals nearly 94 percent acceptance level of vehicle owners and users about the growth of traffic congestion. Despite short distances of the road i.e. 2-4 kilometers and vehicle efficiency, the growth of traffic congestion increases 14036-liters fuel additional consumption. Per month, additional cost of fuel is estimated at 18,808 US dollars for a sum of distance i.e. 72,992 km between residence location and workplace each month. In the case of commuters, the estimation result of the study is 1188 hours of additional time loss with 6706 US dollars’ worth per month. The estimation of total economic loss is 25514 US dollars per month. Specifically, per month, economic loss of doctors and taxi drivers is 6556 US dollars but teachers and bankers have not economic loss.

An Illustrative Study of Vehicle Ad-Hoc Network Security using Advanced Encryption Standard Algorithm

India accounts for the highest road accidents and traffic congestion globally. The necessity for a canny vehicle framework is of great importance. VANET, abbreviated as Vehicular ad hoc networks is a network created in an ad hoc manner where different vehicles can exchange useful information among each other with dedicated servers ensuring safe travel. Security in VANET has always been a challenge in implementing a real time intelligent transport system. VANET is a type of mobile ad-hoc, to give correspondences among close by vehicles and among vehicles and close by fixed hardware. Vehicular ad hoc networks are highly dynamic in nature and suffer from frequent path breakage due to the high velocity of the moving vehicle. Hence, there are many security challenges and different types of attacks that makes VANETs less secure. Therefore, providing secure dedicated short-range communication (DSRC) easefully with any loss of data or malicious nodes has been a major research area. The major concern being addressed in the paper is to provide secure communication and save lives in road accidents. The role of security is high and messages in DSRC send warning messages to other vehicles. If attackers change these messages, then accidents become a part of the network and users’ lives can be at risk. Different classes of attacks include monitoring attack, social attack, timing attack, application attack and network attack to name a few. Advanced encryption standard is a symmetric block encryption algorithm. There is no evidence to crack this algorithm till date. This paper will provide a detailed overview of VANET architecture, types of attacks on VANET, AES algorithm and its salient features and how this algorithm could be utilized to make intelligent transport systems secure.

Intelligent Accident-Detection and Ambulance - Rescue System

Accidents are the major causes of death, be it in today's most technologically advanced world. Road accidents and traffic congestion are the major problems in urban areas. Currently there is no technology for detection of accidents. Also due to the delay in reaching of the ambulance to the accident location and the traffic congestion in between accident location and hospital increases the chances of the death of the victim. There is a need of introducing a system to reduce the loss of life due to accidents and the time taken by the ambulance to reach the hospital. Yearly lots of visually impaired people lose their lives by being victims of such accidents. To overcome the drawback of existing system we will implement the new system in which there is an automatic detection of accident through sensors and it will send the location of the accident to the Nearest Hospital, Police station and to the family members which will rush an ambulance from a nearest hospital to the accident spot. This will minimize the time of ambulance to reach the hospital. To achieve this we are using like MEMS (Micro Electro Mechanical Systems) (MPU6050), And using GPS module we will get the location where the accident took place and by using NODEMCU and IFTTT messaging protocol we will send the message of obtained location. Along with this there would be control of traffic light signals in the path of the ambulance using RFID TECHNOLOGY. This system is fully automated, thus it finds the accident spot, controls the traffic lights, helping to reach the hospital in time.

Adaptive Traffic Management System using CNN (YOLO)

The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control frameworks in India are now needed as it is an open-loop control framework, without any input or detection mechanism. Inductive loops and sensors employed in existing technology used to detect the number of passing vehicles. The way traffic lights are adapted is highly inefficient and costly in this existing technology. The aim was to build a traffic control framework by introducing a system for detection ,which gives an input to the existing system (closed loop control system) in order to adapt to the changing traffic density patterns and to provide the controller with a crucial indication for ongoing activities. By this technique, the improvement of the signals on street is extended and thus saves time by preventing traffic congestion. This study proposes an algorithm for real-time traffic signal control, depending on the traffic flow. In reality, the features of competitive traffic flow at the signposted road crossing are used by computer vision and by machine learning. This is done by the latest, real-time object identification, based on convolutional Neural Networks network called You Look Once (YOLO). Traffic signal phases are then improved by data acquired in order to allow more vehicles to pass safely over minimal wait times, particularly the line long and the time of waiting per vehicle.This adjustable traffic signal timer is used to calculate traffic density utilizing YOLO object identification using live pictures of cameras in intervals and adjusts the signal timers appropriately, therefore decreasing the road traffic congestion, ensuring speedier transit for persons, and reducing fuel consumption. The traffic conditions will improve enormously at a relatively modest cost. Inductive loops are a viable but costly approach. This method thereby cuts expenses and outcomes quickly.

Suburbanisation processes within and outside the city: The development of intra-urban suburbs in Wrocław, Poland

Abstract The scale and the specificity of intra-urban suburbanisation is subject to evaluation in this article, based on a case study of Wrocław city (SW Poland), using data on population changes at an intra-urban scale and on the level of construction activity in the city. Intra-urban suburbanisation is characterised by intensive construction activity and population growth in the peripheral districts of the city, while depopulation takes place in the central part of the city and in large panel block estates from the socialist period. The main factors for the development of intra-urban suburbanisation are a reaction to the unfavourable (from the perspective of the city) suburbanisation processes (outflow of residents and tax revenue, road traffic congestion and the necessity to service populations residing de facto outside the city). The existence of extensive non-urbanised areas within the larger cities of Central and Eastern Europe (identified as potential areas for investment) results from the specific nature of their territorial development in the 20th century, including incorporation processes connected with planned urbanisation.

Delineating traffic congestion zones in cities: An effective approach based on GIS

Identifying congested areas for implementing road pricing is a complicated task, especially as traffic speed and other census data may not be readily available before a detailed feasibility study is commissioned. In this study, we introduce a spatial approach to identify areas with road traffic congestion within cities. The results obtained are validated against the empirical traffic speed data in four Asian cities, i.e. Bangkok, Hong Kong, Kuala Lumpur, Singapore, from 2018 to 2020. Moreover, the findings are also compared with a more detailed conventional method of using job density to identify areas of high traffic congestion. The comparisons show that the spatial approach is both efficient and effective. Through applying the method to London, Milan and Stockholm, the study also demonstrates other potential empirical applications in reviewing the boundaries of existing congestion charging zones.

Multinomial Logit Utility Model for Tanta City Transportation System considering Ridesharing Mode

Recently, ridesharing has been noticed as an effective and sustainable mode of transport. In which, each passenger carries out a journey that benefits travelers and society greatly, such as reducing travel costs, reducing journey times, relieving road traffic congestions, preserving fuel and reducing air pollution. While the importance and efficiency of ridesharing, ridesharing among travelers has not been commonly utilized. This paper introduces an upgraded disaggregated utility model for a mode choice step in the four-step traffic demand model for transport of passengers in Tanta city - Gharbiya governorate. For the first time, the analysis in this paper includes ridesharing mode in addition to other three common modes; private vehicle, bus, and taxi . The aim of this research is to involve the ridesharing mode and new parameters in the mode choice analysis of the case study. This is done by including additional parameters such as travel cost and comfortable in addition to travel time. Based on surveys, the developed Multinomial logit utility models have been assessed using the McFadden pseudo R2 values. The values demonstrated high compatibility of results with real data as pseudo R2 values ranges between 0.2 and 0.4. In addition, all utility models developed for modes are found to have P-values less than 0.05 indicating the significance of the considered utility characteristics.

Urban road congestion patterns under the COVID-19 pandemic: A case study in Shanghai

The novel coronavirus (COVID-19) pandemic has had a significant impact on human mobility around the world. Many cities issued “stay-at-home” orders during the outbreak of COVID-19, and many commuters have also changed their travel modes in the post pandemic period; e.g., transit/bus passengers have switched to driving or car-sharing. Urban road traffic congestion patterns are significantly different than they were pre-pandemic, and understanding such changes can be an opportunity to improve future emergency traffic management and control. Previous studies on this topic have focused on natural disasters or major accidents/incidents. However, very few studies have analyzed the empirical traffic congestion patterns that have occurred during a pandemic. This study takes Shanghai as an example, and conducts a retrospective analysis of empirical spatio-temporal road traffic congestion during the COVID-19 pandemic. The three-month road traffic speed data in the 446 Traffic Analysis Zones (TAZs) collected from Baidu Maps was used in this study. The algorithm of Singular Value Decomposition (SVD) was employed to investigate the inherent composition of the spatio-temporal variation simultaneously influenced by several factors. Three principal components were identified from the spatio-temporal variation, including the stable, main part of variation; the part of the variation that is affected by commuting; and the part of the variation that is affected by migrant populations and the pandemic. The results may suggest ways to improve the emergency management and control of urban roadways in other metropolitan areas worldwide during and after the COVID-19 pandemic period.

Road Traffic Congestion (TraCo) Estimation Using Multi-Layer Continuous Virtual Loop (MCVL)

Any road traffic management application of intelligent transportation systems (ITS) requires traffic characteristics data such as vehicle density, speed, etc. This paper proposes a robust and novel vehicle detection framework known as multi-layer continuous virtual loop (MCVL) that uses computer vision techniques on road traffic video to estimate traffic characteristics. Estimations of traffic data such as speed, area occupancy and an exclusive spatial feature named as corner detail value (CDV) acquired using MCVL are proposed. Further, the estimation of traffic congestion (TraCo) level using these parameters is also presented. The performances of the entire framework and TraCo estimation are evaluated using several benchmark traffic video datasets and the results are presented. The results show that the improved accuracy in vehicle detection process using MCVL subsequently improves the precision of TraCo estimation. This also means that the proposed framework is well suited to applications that need traffic characteristics to update their traffic information system in real time.

A Framework to Reduce Road Congestion by means of Data Analytics

The density of vehicles on the road especially in urban areas keeps on increasing to large amount day by day. Especially during the peak hours of the day, large amount of people wastes much of their time in traffic signals. Not only they waste energy by burning excess fuel and releasing CO2 emissions in the environment as well as their time and money. An idea has been proposed to monitor the traffic congestion by means of data analytics on image data and solve the critical traffic congestion issue. The CCTV or surveillance cameras installed at the top points on the roads acts as a medium to provide image data as an input to analyze road traffic congestion by counting the number of vehicles under specified interval of time. Monitoring of traffic congestion using image processing techniques is very useful for the future urban road planning such as: 1) if there is a need to make the road wider, 2) if there is a need to add more lanes on the road, 3) if there is need to make flyover or a bridge to control the traffic on the roads. It will help municipalities to structure and expansion of the roads.

Analysis of delay and queue length variation at three-leg signalized intersection under mixed traffic condition

The rapid growth in the economy has increased personal vehicle usages which is responsible for hefty road traffic congestion in all major cities. In delay variations at a signalized intersection, there is a significant impact on travel time among the number of various other attributes. Even though the impact of the above-mentioned distinctions on the perspective for design, planning, and analysis of signal control is having an important insinuation nevertheless one of the major problems in the urban area at present scenario is because of the delay due to traffic congestion. Measurement of delay depends upon various parameters such as signal cycle, saturation flow rate, arrival and departure of the class of particular automobiles, types of the intersection, the grade of an intersection, amount of traffic volume, the queue length of traffic, composition of vehicles, and type of traffic control system. Vehicle movement related to a signal indicator, non-stopped movement during the green time, stop and queue movement during red time include a delay that might be due to over-saturation flow in traffic or pedestrian flow, etc. The aim of the paper is to measure the delay variability and find out what amount of delay accounts for the three-leg signalized intersection in total travel time. Delay variability helps in predicting total travel time variability and helps in estimating accurate travel time. It is seen that the stopped delay is the primary affecting constraint in the intersection point which is responsible for the long queue length of the street and it is predominantly founded on the position of the vehicle in the queue line and the manual control of the length of the cycle at a signalized intersection.

Basic Design Architecture of Congestion Notification System: A Real Time Road Traffic Information Enabler

Road traffic congestion is a prominent challenge of today’s urban center. As a push factor of urban centers, it impact negatively on socio-economic well-being of cities. However, contemporary innovative transport technology of Intelligent Transport System (ITS) is bridging the traveler information gaps, through installation and deployment of smart transport infrastructure such as Congestion Notification System at critical traffic intersections and points that aggravate road traffic congestion. This paper therefore provides a detailed explanation on the configuration and basic architecture of a primary Congestion Notification System (CNS) stating its working principles in providing real time road traffic congestion level information to motorist, prior entering the congestion zones or section of the road. Thus, engendering informed decision by motorists on alternative routes rather than the congested route.

Grid Mapping for Road Network Abstraction and Traffic Congestion Identification Based on Probe Vehicle Data

AbstractTraffic congestion monitoring is a long-term concern in urban areas. However, due to the complex structure of urban road networks and large amounts of traffic data, it is necessary to find ...

Performance Analysis of Cellular-Relay Vehicle-to-Vehicle Communications

Vehicle-to-vehicle (V2V) communication-based cooperative vehicular networks are a key technology for future smarter transportation systems as they provide various applications for addressing road safety and traffic congestion. To alleviate the performance limitations of existing dedicated short-range communication (DSRC) protocols, cellular-assisted V2V communications have been proposed recently, wherein cellular base stations (BSs) relay the transmission from one vehicle to another. Such cellular-assisted communications have shown promise for more reliable V2V communications with a wider capacity and longer transmission distances. In this study, we theoretically analyze the performance of cellular-relay (CR) V2V communications, in which a vehicle first transmits a message to its nearest BS via uplink, and then, the BS forwards the message to a destination vehicle via downlink. We model the road segments through a Poisson line process and the positions of vehicles through a Poisson point process on the roads; subsequently, we derive a theoretical expression for the probability of a successful CR transmission and the mean local delay in the CR transmission. Furthermore, we propose a performance metric to evaluate the difference between the performances of CR and direct V2V communications, and it can be applied to automatic transmission mode selection (i.e., CR or direct) for V2V communications. We evaluate the analytical results using numerical examples and demonstrate the impacts of various system parameters on the performances of CR and direct V2V communications.

Effects of Road Traffic Congestion on Economic Activities of Dar es Salaam Region in Tanzania

The general objective of this study was to examine the effects of road traffic congestion on economic activities of Dar es Salaam Region in Tanzania. The findings show that congestion is caused by narrow roads, bad road conditions, rainfall, non-operating traffic lights, double parking, inadequate feeder-roads, road accidents, road work, many pedestrians crossing the road and many bumps in the roads. Also, congestion on average increases costs of fuel by Tsh. 32,167.00 paid from bus drivers’ pocket and reduces productive time of workers by 2.5 hours. Similarly, employees with formal employment got up between 3:00am and 4:00am early in the morning to avoid road congestion but due to unreliable transport services employee with income of Tsh. 90,000.00 spend 39% of the income as fare because they pay twice in a single journey. However, during their return home they are easily caught in congestion as they get home late at 22:00pm. Therefore, congestion affects the economy by reducing the turnover, profit margin and sales of individuals and in aggregates the economic activities of Dar es Salaam region. This study recommends that, roads be enlarged and pedestrian bridges, feeder-roads and bumps be constructed in a way that reduces congestion. Keywords: Road Traffic Congestion and Economic Activities DOI: 10.7176/JESD/12-6-05 Publication date: March 31 st 2021

The Prediction of Urban Road Traffic Congestion by Using a Deep Stacked Long Short-Term Memory Network

Traffic congestion is an overwhelming problem faced by road travelers all over the world. A time-efficient and accurate prediction of upcoming traffic congestion can reduce this problem through enabling the proactive planning of routes. Recent research suggests that prediction accuracy requires the extraction of hidden features of the road network from the historical traffic data. In general, this data is either limited (with a longer sampling time) or not provided by providers. In urban areas, traffic lights, weather conditions, city events, accidents, and people’s habits significantly influence the traffic flow according to the structure of road network. Therefore, a mechanism is required to extract traffic data by scraping images from the route planners’ websites to predict traffic congestion. In this article, we devise such a method and introduce a fuzzy logic and stochastic estimation algorithm to detect congestion levels at the intersections of the road network. We then build a deep stacked long short-term memory network, in combination with online training, for the multipoint future prediction of congestion. We name the proposed model a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fuzzy logic and deep learning-based traffic congestion predictor</i> (FDLTCP) and compare the proposed predictor with the gated recurrent unit and stacked auto-encoders. Experimental evaluations demonstrate the effectiveness of FDLTCP, in terms of mean square error and other critical performance metrics, to perform future predictions.

Spectrum sensing challenges &amp; their solutions in cognitive radio based vehicular networks

SummaryVehicular ad hoc network (VANET) has been established to mitigate road collisions and traffic congestion and provide infotainment facilities to users. Allocated channels to VANET, which is the DSRC (Dedicated Short‐Range Communication), are not adequate for the full implementation of VANET. Cognitive radio (CR) can be used to alleviate this issue. CR is a programmable and intelligent radio system capable of reaching various frequency ranges. CR needs to conduct spectrum sensing to get these bands. A vehicle fitted with a CR can sense the licensed spectrum to locate the vacant spectrum (which is not used by any licensed user) when the DSRC is wholly occupied. Compared to other CR networks, VANET faces specific additional difficulties related to spectrum sensing, such as periodic topological shifts due to high‐speed mobility, multipath fading and shadowing issues, and heterogeneous quality of service (QoS) specifications. This paper explains these problems in depth. All the problems and issues were explored from the perspective of the CR focused on VANET. Probable ideas and directions to overcome these problems have also been presented. We have provided a conceptual framework for spectrum sensing. The framework resolves a variety of issues and concerns that are discussed in the paper. It gives much better results than conventional sensing techniques.

Detection of Road Traffic Congestion Using V2V Communication Based on IoT

Intelligent Transportation Systems (ITS) have been developed to improve the efficiency and safety of road transport by using new technologies for communication. Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) are a subset of ITS widely used to solve different issues associated with transportation in cities. Road traffic congestion is still the most significant problem that causes important economic and productivity damages, as well as increasing environmental effects. This paper introduces an early traffic congestion alert system in a vehicular network, using the internet of things (IoT) and fuzzy logic, for optimizing the traffic and increasing the flow. The proposed system detects critical driving conditions, or any emergency situation blocking the road, and broadcasts remote warnings to the following vehicles. Since not all vehicles are equipped with new technologies, Liquid Crystal Display (LCD) fixed on the roads displays the alert to warn the other vehicles which have neither communication nor sensors. The system was designed with Raspberry Pi 3 Model B equipped with sensors and GPS module to emulate real-world vehicles. The results and observations collected during the experiments showed that the proposed system is able to monitor the road conditions, detect the emergency situation, and broadcast a warning message to the approaching vehicles.