Road Traffic Congestion Research Articles

A Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system

Cities are facing numerous challenges such as road traffic congestion and environmental pollution. Bike-sharing, as an emission-free travel mode, aligns with the principle of green and environmental protection and serves mainly for short-distance trips in urban areas. Shared bikes pose significant issues to operators due to supply–demand imbalances across different time and space. This study proposes a Smart Predict-then-Optimize method for dynamic green bike relocation in the free-floating system, which aims to minimize the cost of fuel and carbon emissions from repositioning vehicles and the total unmet demand during the operating period. A multi-task deep neural network model is designed to predict regional inflow and outflow demand, where targeted modules are embedded to extract the spatio-temporal characteristics. Potential unusable shared bikes are discovered from the users’ travel behavior and the usage characteristics of shared bikes. Then, we build a data-driven optimization model for bike-sharing relocation and design an iterative decomposition algorithm that incorporates an adaptive large neighborhood search for relocation routes and vehicle speed optimization. The proposed method is tested on real-world bike-sharing trips in Shenzhen, China, and results show that the relocation distance and carbon emission cost can be reduced by 11.69% and 14.09% by relocating operational bikes and unusable bikes simultaneously. Additionally, route decision-making with speed optimization can decrease the total fuel and emission, where considering the collection of unusable bikes help improve the service level of the system.

Deep Learning based Artificial Intelligent Systems in Road Traffic Density Estimation and Congestion Classification

Deep Learning based Artificial Intelligent Systems in Road Traffic Density Estimation and Congestion Classification

The impact of urban road pricing on the use of bike sharing

We empirically analyse the effect of road pricing on the use of bike sharing. We exploit two sudden policy changes in Milan's congestion pricing scheme resulting in a shift from priced to unpriced road use: a temporary suspension of the scheme in 2012, and a durable reduction in its application schedule. We find that the sudden removal of road pricing decreases bike-sharing use by about 5%. The policy impact mainly occurs through the increase of road traffic congestion, which makes cycling less safe and pleasant. The reduction in the schedule leads to an 8% decline in bike-sharing use in the affected time window. Our findings indicate that policies inducing drivers to internalise the external costs of car use also promote the uptake of non-polluting transport modes. This widens the spectrum of benefits that need to be considered in the design and implementation of road pricing policies.

A reliable position-based routing scheme for controlling excessive data dissemination in vehicular ad-hoc networks

In the past two decades, the automotive industry has undergone tremendous changes, as this field has become one of the fastest developing and growing fields, especially with the progress of global digitization and massive research related to networks. So, it comes as no surprise that self-driving vehicles are ubiquitous mainly relying on Vehicular ad-hoc networks (VANETs). This transformation ensures improved road navigation and traffic congestion avoidance by relying on Rapid Data Deployment (DD). On the other hand, although DD achieves high connection reliability, it may affect network bandwidth and performance. In addition, excessive DD causes frequent link outages resulting in reduced data delivery, massive packet loss, and premature end of network life. This paper presents an integrated architecture proposal for a vehicle dynamic assistance architecture, based on reliable methods to ensure steering accuracy while minimizing the energy expended for controlling the DD rate via VANETs. The proposed architecture integrates between Software-Defined Networks (SDN) and fog computing based on dealing with the mobility factors that exploit vehicle communication behaviors. Such integration will aid in improving network performance in terms of packet delivery and DD. The study also discusses how to take into consideration the Euclidean distance, geographical routing information, residual power ratio, and latency time to maximize network stability and avoid possible link disruption. The simulation results prove that there is a 62% to 70% enhancement of the whole power consumption and network throughput, depending on the implementation of the proposed position-based routing approach. Interestingly, the proposed routing protocol is a dual-phase routing protocol with a 90% of SDN data packet delivery ratio and an 82% of SDN data loss reduction. So, when the SDN fails to deliver packets, the proposed position-based routing handles them as a parallel mechanism of SDN.

A Survey on Traffic Modeling and Mitigation Techniques

Abstract—The growing road traffic congestion in India is becoming more prevalent nowadays, the worst hit being the metropolitan and developed cities. Its ever-increasing nature poses a threat to development and requires quick action to help ease the troubles faced by people on a day-to-day basis. Modern technology offers a great aid to these efforts by providing mechanisms to model the travel patterns of a region and provide quick congestion relief by introducing adaptive traffic signals that operate based on the current traffic density of the road. In this paper, we present a survey of the most popular techniques involving travel demand modeling and adaptive signal control and compare the various methods employed by researchers to tackle both of these issues separately. The survey focuses on analyzing these methods from the perspective of implementation in an Indian scenario and offering a theoretical solution that involves the use of both these techniques to ease the problem of traffic congestion. Index Terms—road traffic congestion mitigation, adaptive traf- fic signal, travel demand modeling,

An Investigation of the Economic Implication of Road Traffic Congestion on the Economy of Kebbi State, Nigeria

An Investigation of the Economic Implication of Road Traffic Congestion on the Economy of Kebbi State, Nigeria

The Impact of Road Functions on Road Congestions Based on POI Clustering: An Empirical Analysis in Xi’an, China

In new-tier cities with rapid urbanization, the reorganization of urban spatial functions and the development of road networks have brought novel challenges to traffic congestion control. Urban land use patterns have a significant correlation with urban traffic congestion. However, whether and how land use patterns of cities close to the roads affect road congestion is less to be discussed. This article investigated the relationship between land use patterns close to the urban trunk road network and traffic congestion in new tier cities Xi’an, China. We adopted the DBSCAN algorithm to cluster POIs and use the mixed POI clusters to label the socio-economic functions of roads. We found the spatial heterogeneity of POIs on the trunk road network and identified the impacts of the scales and types of POI on road congestion based on the empirical analysis. Compared to the POIs as origin and destination of the trips, the POIs as stopover points of the trips cause significantly more road congestion. The POIs with bidirectional flows at entrances/exits are more likely to cause road congestion than the POIs with unidirectional flows. Moreover, the POIs with flexible traffic flows increase road congestion, while the POIs with predicted traffic flows have no statistically significant correlation with road congestion. The results help urban planners to plan the scale, type, and location of POIs close to roads and to optimize the socio-economic functions of roads and alleviate road congestion.

Tolls vs tradable permits for managing travel on a bimodal congested network with variable capacities and demands

Congestion pricing has long been considered an efficient tool for tackling road traffic congestion, but tolls are generally unpopular. Interest is growing in tradable permits as an alternative. Tolls and tradable permits are interchangeable if travel conditions are unchanging, but not if conditions vary and tolls and permit quantities are inflexible and cannot be adapted to current conditions. We compare the allocative efficiency of tolls and tradable permits on a bimodal network under uncertainty. Road links are congestion prone and public transit may be crowded. Road traffic entering a cordon area around the downtown is controlled using either a toll or a tradable permit. Two groups of travelers can drive or take transit. Group 1 travels downtown, and if it drives it must either pay the toll or use a permit. Group 2 travels to a suburb, and can avoid the cordon by taking a bypass. All demand and cost parameters of the model can vary, either systematically or irregularly. Each parameter combination constitutes a state. Travelers learn the state in advance, and adapt their mode and route choices accordingly. A planner minimizes expected total travel costs by either setting the level of the toll or choosing the quota of permits to distribute. Two cases are considered. In the first, the toll and quota are flexible and can be adjusted to daily travel conditions. In the second, the instruments are inflexible and must be set at the same level regardless of the state. If travelers have identical preferences, the optimal flexible toll is invariant to the numbers of travelers in each group and the capacity of the link entering the cordon. The toll is robust in the sense that inflexibility causes no welfare loss if these parameters vary. By contrast, the quota is not robust.We derive a general rule for ranking the efficiency of a fixed toll and fixed quota. We then explore a numerical example. In most instances, the fixed toll outperforms the fixed quota by a significant margin although the quota can do better in some states. We also compare the welfare-distributional effects of tolls and permits, and find that suburban travelers fare better than downtown travelers from both forms of regulation.

Smart cities at risk: Systemic risk drivers in the blind spot of long-term governance.

In this article, we analyze "digital massification" in smart cities, that is, an ever-growing number of market participants, consumers, and Internet of Things devices with simultaneous accommodation of users to increasing disturbances and inconveniences due to data congestion-as a driver for systemic risk. We argue that digital massification phenomena largely escape societal awareness due to their protracted evolution and are therefore still in the blind spot of long-term governance. Our analysis makes methodological use of historical and relational analogy, and we introduce and elaborate concepts and terms that allow us to discuss the evolutionary nature of massification, that is, the foreseeable increasing probability of the occurrence of trigger events. Using the analogy to the history of road traffic congestion, we deduce that digital massification will most likely lead to a future "risk transition" where tolerated disturbances and inconveniences of the present will turn into systemic impacts. This insight calls for heightened sensitivity in governance to massification phenomena to ensure the long-term resilience of smart cities.

Privacy-preserving Authentication Scheme for VANETs using Blockchain Technology

Road accidents and traffic congestion severely impact road safety, resulting in significant loss of life and property. Therefore, there is a need for road safety and traffic management. The wireless networks of connected vehicles called Vehicular Adhoc Networks (VANETs) are designed to improve the traffic management on highways with comfort and safety for drivers and passengers. However, there is a need to develop an authentication scheme that can protect the privacy of vehicles and secure them against attacks such as insider attacks and repudiation attacks. In this article, we present a scheme that performs vehicle registration and message forwarding in the VANETs and appends the transactions to the blockchain. The proposed privacy-preserving authentication scheme using blockchain technology enables vehicles and Trusted Authorities (TA) to authenticate vehicles while preserving privacy. The proposed scheme facilitates privacy-preserving authentication by deploying the scheme on a decentralized network. The proposed scheme ensures privacy, confidentiality, integrity, and transparency during vehicle authentication for VANETs. In the proposed scheme, an authentication process is automated with the use of a smart contract. We comparatively evaluate the security and performance of the proposed privacy-preserving authentication scheme with the state-of-the-art literature. The proposed privacy-preserving authentication scheme is significantly reliable and transparent in authenticating vehicles while preserving privacy.

Regression based neural network model for prediction of road traffic congestion : A case study of Bhubaneswar

The prediction of road traffic congestion is the most important and essential aspect to reduce the suffering of population of urban cities which is primarily carried by roads. The lack of a traffic congestion data unavailability and evaluation standard makes the effect of traffic congestion prediction more difficult and worsen. Traffic congestion occurs due to increase in number of vehicles on roads which reduces speed of vehicles, increases delay time, and increasing vehicular queuing in traffic. Due to traffic congestion, not only delaying time increases but also its threat to slower down the economic growth rate of our country and also have high impact on our personal growth, living condition with high level of pollution and undesirable feature of overloaded streets. Traffic congestion predicting modeling plays very important role so we need a innovative approach to predicting the congestion on roads. In this paper, we aim to provide a model which studies the real time environment characteristics of the road, and analyzed the data, congestion location identification, directional movement of all those locations surveyed and forecasting traffic location where traffic congestion may occur in near future.

Road traffic analysis on the congestion problem using the Ford-Fulkerson algorithm

In this paper, the well-known Ford-Fulkerson algorithm in graph theory is used to determine the maximum flow in a road traffic network. Road traffic congestion is a major urban transport problem that occurs when the volume of traffic exceeds the capacity of existing road facilities. The manifestation of traffic congestion is due to the ownership of a high number of vehicles at the expense of road traffic infrastructure and the high growth of the urban population. Only one road connects the city center of Kinshasa, which concentrates the main activities of the inhabitants, to the international airport of Ndjili. Whether you are a pedestrian, a car driver or a public transport user, you cannot escape the traffic jams that refuse to leave Kinshasa. Even motorbike taxis cannot escape. Yet the grade separations built on Lumumba Boulevard in Debonhomme, at the Marché de la Liberté and at Pascal are all open to traffic. Built to facilitate the flow of traffic between the city center (Gombe) and N'djili International Airport, these elephantine structures serve little purpose. In this study, the identification of the maximum flow and bottleneck path along the Lumumba Boulevard between Debonhomme and Ndjili International Airport in the Tshangu district of Kinshasa was carried out. All possible routes from the different sources to the different wells were established. It emerged from this study that the optimal solutions would be the construction of secondary roads and improvement of the road facilities to minimize the problem of traffic congestion.

Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control for Intelligent Transportation Systems in Smart Cities

Abstract In the last few years, some progress had been made in smart cities, and reduction in traffic congestion was the topmost concern in the development of smart cities. Shorter delays in transmission between Roadside Units (RSUs) and vehicles, road safety, and smooth traffic flow are the major difficulties of Intelligent Transportation Systems (ITS). The rapid improvement in automobiles occurs which increased the number of road accidents and traffic congestion. Machine Learning (ML) was an advanced technique to find hidden insights into ITSs without being explicitly programmed by learning from datasets. This article introduces an Improved Arithmetic Optimization with Deep Learning Driven Traffic Congestion Control (IAOADL-TCC) for ITS in Smart Cities. The presented IAOADL-TCC model enables traffic data collection and route traffic on existing routes for avoiding traffic congestion in smart cities. To accomplish this, the IAOADL-TCC model employs hybrid convolution neural network attention long short-term memory (HCNN-ALSTM) method for traffic congestion control. In addition, IAOA based hyperparameter tuning strategy is derived to optimally modify the hyperparameter values of the HCNN-ALSTM model. The presented IAOADL-TCC model effectively enhances the flow of traffic and reduces congestion. The experimental result study of the IAOADL-TCC method can be tested by making use of road traffic dataset from Kaggle repository. The experimental outcome stated the better performance of the IAOADL-TCC model over other DL methods.

Analysis of Intelligent Transportation System Application Based on Internet of Things and Big Data Technology under the Background of Information Society

With the growth of national economic strength, urban modernization has become an indispensable part. Among them, urban road traffic congestion has become the main problem affecting work and traffic. Traffic pressure promotes the massive use of Internet of Things and other technologies in the transportation system. The integration of computer, Internet of Things and transportation system forms a new intelligent transportation concept. With the advent of intelligent information society, the application of intelligent transportation is imminent. In view of the shortcomings of urban intelligent transportation, this paper uses Internet of Things technology and big data technology to study it. Firstly, it analyzes the function and value of IoT intelligent transportation, analyzes the system components from the construction principle, and makes full use of intelligent technology to improve the transportation system. Secondly, the optimal path algorithm is proposed in the IoT urban intelligent transportation system model. The advantages and disadvantages of genetic algorithm and heuristic algorithm are analyzed, and ant colony algorithm is used for further optimization. In order to verify the feasibility of the intelligent transportation system, technologies such as big data edge computing are also used to comprehensively evaluate and study the use of the system. Finally, we analyze the results of the practical application of the intelligent transportation system under the Internet of Things technology. The research results show that the urban intelligent transportation system, supported by the Internet of Things technology, can realize the dynamic planning of the optimal route, help the people to improve their driving experience in the high safety and intellectualization, and greatly avoid a series of problems caused by congestion.

Smart Traffic Scheduling for Crowded Cities Road Networks

With the fast-expanding number of vehicles in smart cities, the management of road intersections and traffic congestion has grown to be major problems. Drivers often express their opinion that putting traffic lights on while taking traffic flows into account will have a significant impact on how traffic moves. This paper presents a smart Road traffic Control management system termed Urban Traffic Control (UTC) keeping real-time dynamic traffic flow in mind which helps in upgrading the level of road traffic network management. To provide an organized traffic arrangement, UTC presents methodologies such as vehicle counting, controlling process, and evaluation of lanes keeping status in mind, this whole procedure is implemented by taking the complete traffic network into an account instead of just considering intersections. The primary goal of our system is to lessen traffic jams by cutting down on the trip and waiting times vehicles spend at crossings and intersections. We need to assign a plan for traffic flow that has the least amount of traffic congestion and vehicle waiting time, for this purpose some indicators and models are introduced in this study. Lane weight, traffic jam indicator, and vehicle priority are among these models. As this work is an improvement on the current Road Network without much changing, we integrate our system on normal traffic lights which allow each lane a chance to move and we also considered the no-interference lane movement. To simulate our idea, we introduced a smart road traffic control system consisting of multi-agents, by using a NetLogo stimulator. To compare the fixed cycle traffic light, several vehicles (150 in total) with random behaviour were generated and scattered over 25 different intersections for the time duration of 9 h. This setting was used to test our smart traffic control solution on both lane flow and no interference movement flow. According to the obtained results, there was a 25.98% reduction in total average waiting time over simulation period for all vehicles and a reduction of 34.16% for no interference movement flow. These observations clearly state that suggested method is better suited for today's complex traffic conditions where change in infrastructure is minimal.

Evaluating the Congestion Relief Impacts of Public Transport in Monetary Terms

Traffic congestion is a major urban transport problem. Efficient public transport (PT) can be one of the potential solutions to the problem of urban road traffic congestion. Public transport systems can carry a significant amount of trips during congested hours, improving overall transportation capacity, and can release the burden of excess demand on congested road networks. This paper presents a comparative assessment of international research valuing the congestion relief impacts of PT. It explores previous research valuing congestion relief impacts and examines secondary evidence demonstrating changes in mode split associated with changes in public transport. The research establishes a framework for estimating the monetary value of the congestion reduction impacts of public transport. Congestion relief impacts are valued at between 4.4 and 151.4 cents (Aus$, 2008) per marginal vehicle km of travel, with an average of 45.0 cents. Valuations are higher for circumstances with greater degrees of traffic congestion and also where both travel time and vehicle operating cost savings are considered. A simplified congestion relief valuation model is presented to estimate the congestion relief benefits of PT based on readily-available transport data. Using the average congestion valuation and mode shift evidence, the model has been applied to a number of cities to estimate the monetary value of the congestion relief impact of public transport. Overall, the analysis presents a simplified method to investigate the impact of public transport on traffic congestion. Further research is warranted to develop a comprehensive approach for establishing a measure of the congestion relief impact of public transport.

SOCIO-ECONOMIC AND ENVIRONMENTAL IMPLICATIONS OF ROADSIDE AUTOMOBILE WORKSHOPS IN OTA CITY, NIGERIA

Roadside Automobile Workshop (RAW) activities remain principal components of the informal sector in many Nigerian cities. Its emergence is attributed to the advent of automobiles and their use in easing urban mobility. Expectedly, unlike the formal auto-workshops, the RAW are located across the available spatial places where, motorists find solace to immediate respond to maintenance and other mechanical faults/malfunctions of their vehicles. However, the spatial locations and operations of RAW, along with the accompanying adverse effects, are recurring issues of concern in many cities in Nigeria. In this regard, this study assessed the socio-economic and environmental implications of RAW in the city of Ota, Nigeria. A cross-section survey research is adopted in this study, while 400 residents were sampled using multistage sampling technique. Major findings show that most of the residents have spent over four years in study area. It also revealed that increase loss of green area, noise pollution, and land vibration are the top-ranked environmental implications, while increase road traffic congestion, untrained technicians, and disruption of traffic flow are top-ranked economic implications. However, improve access to maintenance workshop, reduce vehicular breakdown on roads are top-ranked social implications of RAWs. This study concludes that the indiscriminate location of RAWs in the study area is detrimental to socio-economic development of study area (F10 38923.851, p=0.000<0.05), and recommends policy implications and strategies to improve the activities of RAW in the study area.

Economic Problems with Subsidies for Electric Vehicles

The use of motor vehicles in Australia generates external costs. The external costs are associated with government expenditure on road infrastructure, traffic congestion and road accidents as well as pollution. A subsidy for electric vehicles will reduce the pollution external cost, and more so as the share of electricity generated from fossil fuels declines. However, the subsidy‐induced increase in motor vehicle use will increase the other external costs. Also, the subsidy and the fall in excise tax revenue collected will add to the budget deficit. The phase‐in of electric vehicles provides an opportunity to reform special taxes and charges on the use of motor vehicles. The current ad hoc and long time ago imposed government taxes and charges on motor vehicles should be replaced with explicit taxes to internalise each of the external costs of motor vehicles.

A Traffic Management System for Minimization of Intersection Traffic Congestion: Case Bengkong Junction, Batam

Purpose: An increase in the number of vehicles that are not matched by the growth of road area is one of the causes of road Traffic congestion in Batam City. Traffic congestion occurs, especially in business and densely populated residential areas. The government has carried out various engineering and traffic management to keep the road service level under control. This study aims to analyze traffic management's efficiency in reducing Traffic congestion on roads in Batam City.
 Design/methodology/approach: This study uses a case study at the Bengkong red light intersection, Batam City. The analysis was carried out using The Indonesian Highway Capacity Manual-1997.
 Findings: Traffic management and engineering must be implemented gradually to get a suitable implementation model. Actual conditions in the field become essential in determining the right choice of traffic management and engineering.
 Research limitations/implications: Traffic management and engineering analyzes were conducted locally at the case study site.
 Practical implications: The analysis of traffic management reduces traffic congestion's impact.
 Originality/value: Original paper
 Paper type: A case study

A route network planning method for urban air delivery

High-tech giants and start-ups are investing in drone technologies to provide urban air delivery service, which is expected to solve the last-mile problem and mitigate road traffic congestion. However, air delivery service will not scale up without proper traffic management for drones in dense urban environment. Currently, a range of Concepts of Operations (ConOps) for unmanned aircraft system traffic management (UTM) are being proposed and evaluated by researchers, operators, and regulators. Among these, the tube-based (or corridor-based) ConOps has emerged in operations in some regions of the world for drone deliveries and is expected to continue serving certain scenarios that with dense and complex airspace and requires centralized control in the future. Towards the tube-based ConOps, we develop a route network planning method to design routes (tubes) in a complex urban environment in this paper. In this method, we propose a priority structure to decouple the network planning problem, which is NP-hard, into single-path planning problems. We also introduce a novel space cost function to enable the design of dense and aligned routes in a network. The proposed method is tested on various scenarios and compared with other state-of-the-art methods. Results show that our method can generate near-optimal route networks with significant computational time-savings.