Traffic Congestion In Cities Research Articles

A multiobjective model for the green capacitated location-routing problem considering drivers' satisfaction and time window with uncertain demand.

Location-routing problem is a combination of facility location problem and vehicle routing problem. Numerous logistics problems have been extended to investigate greenhouse issues and costs related to the environmental impact of transportation activities. The green capacitated locating-routing problem (LRP) seeks to find the best places to establish facilities and simultaneously design routes to satisfy customers' stochastic demand with minimum total operating costs and total emitted carbon dioxide. In this paper, features that make the problem more practical are: considering time windows for customers and drivers, assuming city traffic congestion to calculate travel time along the edges, and dealing with capacitated warehouses and vehicles. The main novelty of this study is to combine the mentioned features and consider the problem closer to the real-world case uses. A mixed-integer programming model has been developed and scenario production method is used to solve this stochastic model. Since the problem belongs to the class of NP-hard problems, a combination of the progressive hedging algorithm (PHA) and genetic algorithm (GA) is considered to solve large-scale problems. It is the first time, as per our knowledge, that this combination is implemented on a green capacitated location routing problem (G-CLPR) and resulted in satisfactory solutions. Nondominating sorting genetic algorithm II (NSGA-II) and epsilon constraints methods are used to face with the bi-objective problem. Finally, sensitivity analysis is performed on the problem's input parameters and the efficiency of the proposed method is measured. Comparing the results of the proposed solution approach with those of the exact method indicates that the solution approach is computationally efficient in finding promising solutions.

Intelligent workload allocation in IoT–Fog–cloud architecture towards mobile edge computing

Because of the tremendous growth in the number of smart vehicular devices and 5G mobile technologies, the Internet of Things (IoT) has experienced rapid expansion. This has led to a considerable increase in the volume of sensory data produced from, but not limited to, monitoring devices, traffic congestion in cities, safety, and pollution control. Cloud computing can deal with the corresponding workload by providing virtually unlimited computational resources. But, given the importance of the quality of service and security in delay-sensitive requests, other solutions like fog computing have also been introduced to speed up processing and management of sensory data in real scenarios like smart grid and IoT. Processing workloads at the network edge reduces the delay in mobile edge computing, but it highly increases the consuming power. Therefore, there is an urgent need for the improvement of the energy model of fog devices at the network edge. This paper is an attempt to modify this model using the green energy concept and reduce both delay and power consumption in multi-sensorial frameworks in secure IoT systems. In the proposed method, a Genetic Algorithm (GA) is used for handling a large number of requests and the corresponding quality and security limitations. Simulation results show that the proposed method can simultaneously reduce the delay and the power consumption of edge devices compared to a baseline strategy.

Effect of combined strategy on mitigating air pollution in China.

Air pollution has worsened as a result of increased traffic congestion in cities. Using air pollution caused by motor vehicle emissions (mainly by carbon monoxide, hydrocarbon, nitrogen oxide, and particulate matter) as an example, in this study, we applied an integrated algorithm comprising system dynamics, entropy weight method, and gray system theory to establish a weighted logic function. A vehicle pollutant control model (including the transport, health, and environmental subsystems) was established by VENSIM software. The medium- and long-term key variable analysis showed that the integrated algorithm, fully reflecting the advantages of system dynamics and gray system theory, more accurately described air pollution caused by vehicle emissions. Our research results also revealed that the combined strategy of penalties and air pollution charging fee had a threefold effect: reduced congestion and emissions, improved health impact index, and reduced number of illegal trips. Specifically, the degree of traffic congestion, number of illegal trips, and the degree of air pollution decreased by approximately 82.40%, 69.35%, and 68.91%, respectively, whereas the health impact index increased by about 279.03%. This finding provides guidance for improving policy and optimizing management and control modes.Graphic abstract

Study of urban-traffic congestion based on Google Maps API: the case of Boston

Abstract Urbanization growth, together with the limited capacity of the road network, has worsened traffic congestion inside the cities. To improve the urban traffic conditions, it is essential to better understand and measure urban-traffic behavior not only on different period time of a day (e.g., morning or evening peaks) but also on different days (i.e., Monday to Sunday). Using real and recent data from the Google Maps API, this paper proposes a new approach to estimate the speeds within defined geographical areas (i.e. zip codes) per daytime and per weekday. Using this input a statistical analysis including k-means clustering is adopted to classify and define different urban-congestion levels according to the estimated speeds the number of inhabitants the zone types and the type of roads in each zip code. In order to validate our approach, we conduct an experimental analysis in Boston, US. Our results provide managerial insights for key stakeholders (i.e., Carriers, Consumers, and Government) to improve the efficiency of the road network and reduce traffic congestion in cities.

Economic and technical classification of the city traffic congestion levels

Economic and technical classification of the city traffic congestion levels

MODE CHOICE BEHAVIOR FOR GROUND ACCESS TO AIRPORTS: A CASE OF ISTANBUL ATATÜRK INTERNATIONAL AIRPORT (IST) AND SABIHA GOKCEN INTERNATIONAL AIRPORT (SAW)

Accessing airports has been a problem for passengers due to traffic congestion in cities. Thus, in order to catch the flights on time, passengers consider many factors to choose the transportation mode for airport access. Some of the factors are covariates, and they can be listed as age of the passenger, number of luggage carried, travel cost and travel time to the airport, group size of the passengers, and reliability of access modes. These covariates, may differ for each airport; and hence, in this paper, we investigated the differences of these covariates between Istanbul Ataturk International Airport (IST) and Istanbul Sabiha Gokcen International Airport (SAW). Analysis of Variance (ANOVA) and post-hoc tests were conducted to investigate the covariates that affect the mode choice to access IST and SAW. The results indicated that these two airports have differences in terms of the effect of these selected six covariates on airport access mode choice. Reliability of the modes, travel time, travel cost, age of the passenger and traveling group size affected the mode choice to access IST. On the other hand, only reliability of the modes, travel time and travel cost had effects on airport access mode choice for SAW. Based on the analysis, some recommendations for the decision makers were also provided.

Swarm Based Intelligent Transportation Systems Using Internet of Things in Vehicular Ad-Hoc Network

The increasing current advancement of digital technology is completely based on Internet of Things (IoT). The IoT is used to facilitate smart city traffic congestion through Vehicular Ad-Hoc Network (VANET). In the Smart city, multiplying the number of vehicles has brought focus on road safety precautions and communication between vehicles. It is a precise time to concentrate on the new advancement of modern applications and its related services towards the vehicular environments. The Manual traffic systems are not sufficient for the increasing modernization of the city. The VANET is a spontaneous Ad-Hoc network formed over vehicles wandering on the road. In this research work states that the overview of various IoT based routing methodologies and challenges towards the VANET routing through intelligent optimization. The Swarm based intelligent algorithms for Vehicular Ad-Hoc networks deals with latency, data throughput, data delivery cost and data delivery ratio of the intelligent system. Swarm intelligent algorithms are used for optimization of intelligent transport system. It is a collective behavioral system of nature inspired ant/bee activity.

Estimation of traffic density from drone-based delivery in very low level urban airspace

Driven by rising consumer demand, interest is growing in the application of autonomous unmanned aerial vehicles (drones) for the last-mile delivery of small express packages and fast-food meals in cities. To be realised, this would require the Very Low Level (VLL) urban airspace to be able to cope with high traffic densities of commercial delivery drones. The potential benefits of such novel drone-based applications are a reduction of traffic congestion in cities, lower greenhouse gas emissions and more efficient transportation operations. To help realise this concept, programs such as U-Space, the unmanned traffic management system for Europe, are developing important services such as deconfliction management and dynamic capacity management. However, for several of these services, design choices will depend on how, and how extensive they will be used. It therefore becomes important to estimate how many delivery drones would operate in a typical city. This paper aims to provide an estimate by establishing a framework to determine the traffic demand for express drone-based package delivery of five European countries. In addition, a detailed case-study is presented for determining traffic density of express package drone delivery for Paris metropolitan area in order to assess the feasibility from a user's perspective. The paper also discusses the potential of fast-food meal delivery drones compared to traditional delivery modes for Paris. Results suggest that hourly traffic densities culminating from express package and fast-food meal delivery drones will exceed today's global commercial aircraft traffic of 10,000 per day by more than six-fold for just one potential metropolitan city.

An Efficient Adaptive Traffic Light Control System for Urban Road Traffic Congestion Reduction in Smart Cities

Traffic lights have been used for decades to control and manage traffic flows crossing road intersections to increase traffic efficiency and road safety. However, relying on fixed time cycles may not be ideal in dealing with the increasing congestion level in cities. Therefore, we propose a new Adaptive Traffic Light Control System (ATLCS) to assist traffic management authorities in efficiently dealing with traffic congestion in cities. The main idea of our ATLCS consists in synchronizing a number of traffic lights controlling consecutive junctions by creating a delay between the times at which each of them switches to green in a given direction. Such a delay is dynamically updated based on the number of vehicles waiting at each junction, thereby allowing vehicles leaving the city centre to travel a long distance without stopping (i.e., minimizing the number of occurrences of the ‘stop and go’ phenomenon), which in turn reduces their travel time as well. The performance evaluation of our ATLCS has shown that the average travel time of vehicles traveling in the synchronized direction has been significantly reduced (by up to 39%) compared to non-synchronized fixed time Traffic Light Control Systems. Moreover, the overall achieved improvement across the simulated road network was 17%.

Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection.

As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.

A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places

Rising interest in the field of Intelligent Transportation Systems combined with the increased availability of collected data allows the study of different methods for prevention of traffic congestion in cities. A common need in all of these methods is the use of traffic predictions for supporting planning and operation of the traffic lights and traffic management schemes. This paper focuses on comparing the forecasting effectiveness of three machine learning models, namely Random Forests, Support Vector Regression, and Multilayer Perceptron—in addition to Multiple Linear Regression—using probe data collected from the road network of Thessaloniki, Greece. The comparison was conducted with multiple tests clustered in three types of scenarios. The first scenario tests the algorithms on specific randomly selected dates on different randomly selected roads. The second scenario tests the algorithms on randomly selected roads over eight consecutive 15 min intervals; the third scenario tests the algorithms on random roads for the duration of a whole day. The experimental results show that while the Support Vector Regression model performs best at stable conditions with minor variations, the Multilayer Perceptron model adapts better to circumstances with greater variations, in addition to having the most near-zero errors.

A service demand forecasting model for one-way electric car-sharing systems combining long short-term memory networks with Granger causality test

Abstract As an emerging green and sustainable transportation system, electric car-sharing helps reduce environmental pollution, carbon emissions, and traffic congestion in cities. An accurate service demand forecasting model is essential for system operators to improve vehicle relocation efficiency so as to satisfy uncertain user trip demand. Therefore, this paper studies relevant indicators affecting car-sharing service demand at the operational level and constructs a micro demand forecasting model for one-way electric car-sharing systems. Taking the city of Shanghai as a case study, firstly, 11 raw indicators are hypothesized to be influential when predicting the order volume of car-sharing service and four 11-variate vector autoregression systems under different space-time conditions are established and estimated. Then significant indicators are achieved from 11 raw indicators by conducting Granger causality test. After that, the exact effect mechanism between each indicator and order volume is analyzed by impulse function and the contribution of each indicator to the error of prediction is calculated by variance decomposition. Finally, long short-term memory network is adopted to train historical data of significant indicators to get an accurate prediction of service demand in different stations and time periods. Major findings of this research are (1) Indicators including ‘Driving distance’, ‘Weekday’, ‘Daily highest temperature’, ‘Daily lowest temperature’, and ‘Fee per kilometer’ have no ‘predictive power’ for the demand forecasting model; (2) ‘Car pick-up interval’, ‘Trip time’, ‘Workday or not’, ‘Daily weather condition’, ‘Fee per minute’, and ‘Order volume’ itself are significant indicators but the effect of ‘Workday or not’ and ‘Daily weather condition’ are negligible; (3) The forecasting model based on long short-term network has better prediction performance than other forecasting models; (4) It would be better to exclude irrelevant indicators before constructing the forecasting model.

Effectiveness of Ring Roads in Reducing Traffic Congestion in Cities for Long Run: Big Almaty Ring Road Case Study

It is common to increase road capacity by constructing ring roads to reduce traffic congestion in city areas, although this is often found to be ineffective in the long run. Accordingly, this study investigates various traffic congestion management approaches and their effectiveness in major cities, and explores an identical transport problem in Almaty, Kazakhstan: The Big Almaty Ring Road (BAKAD). Several case examples from the existing literature are examined in which various approaches were taken for managing traffic congestion problems, and these approaches are classified into three concepts. The first concept comprises heavy engineering measures such as ring road development, new road construction, expansion of existing roads, etc. Such measures can initially reduce traffic congestion, but often become ineffective with time due to the generation of induced traffic. Many cities have taken Push and Pull measures that ensure more efficient use of existing capacity and have initiated environmentally friendly alternative transportation modes such as decreased car usage; promotion of public transport, biking and walking; minimization of the necessity of people’s movement by changing urban land use patterns; and so on. These approaches have been found to be effective in providing sustainable transportation solutions and are classified as concept 2. Nevertheless, Push and Pull measures might not be enough for managing traffic congestion, and it might be necessary to increase the road capacity through heavy engineering measures, especially if the city experiences heavy transit traffic. This combined approach is categorized as concept 3. Consequently, the BAKAD project is examined under the umbrella of three concepts, and recommendations are provided based on the findings from the experience of different cities and interviews with experts from Almaty city. Both the results and recommendations developed are relevant for this specific case only, and are not necessarily transferable.

Optimizing the spatial relocation of hospitals to reduce urban traffic congestion: A case study of Beijing

AbstractTraffic congestion represents an ongoing serious issue in many large cities. Many public facilities, such as hospitals, tend to be centrally located to ensure they are most accessible to local residents; as a result, they may contribute significantly to a city's traffic congestion. In this study, a multi‐objective spatial optimization model was provided to help formulate hospital relocation plans, taking into account both traffic congestion and hospital accessibility. Using intra‐urban movement data, we proposed a method to estimate the area‐wide traffic congestion caused by hospital visits and to identify potential hospitals to be relocated. An NSGA‐II (Non‐dominated Sorting Genetic Algorithm II) algorithm was applied to solve the hospital relocation optimization problem; we applied our model to study optimal hospital relocation plans in Beijing. Analysis results provide a tradeoff between traffic congestion relief and hospital accessibility. We discussed plans that significantly reduce traffic congestion while maintaining a high level of hospital accessibility. Our study has significant policy implications and provides insights for future facility planning and transportation planning.

Recurrent city traffic congestion propagation analysis

In order to analyze the traffic congestion of urban road network, the data preprocessing method is improved, and the three-dimensional spatio-temporal state data is transformed into one-dimensional data, and the spatio-temporal constraint is added on the basis of FP-Growth algorithm to achieve more efficient mining congestion propagation law. The purpose of this paper is to study the traffic congestion propagation law in the morning and evening peaks of the working day and all day and rest days through two experiments, and obtain the traffic congestion propagation law in working days and rest days. The research results show that using the urban road network traffic state data, the analysis method of this paper can more effectively mine the propagation law of recurrent traffic congestion.

Research on the Construction of Intelligent Transportation System Based on Internet of Things

At present, with the development of economy, people's living standards are getting higher and higher. Based on the current big city traffic congestion phenomenon, by introducing the concept of Internet and related technologies, the five layers of building based on Internet of things a subject three division wisdom metropolitan transportation system architecture, this architecture includes homework layer, perception layer, network layer, application layer and user layer, a main body is a big city traffic database is the core of the information transmission, three departments include traffic police supervision department, city management and cost management departments. Through this framework, the whole process of urban traffic information can be controlled in real time, so as to guarantee the demand of urban consumers and residents and promote the stable and healthy development of transportation in China's big cities. This paper USES the knowledge of Internet of things to build the intelligent transportation system of big cities, hoping to get inspiration from it.

GeoSim 2018 workshop report the 1st ACM SIGSPATIAL international workshop on geospatial simulation

Geospatial simulation is a powerful tool for many disciplines including urban analytics, social science and meteorology to understand, explain, and predict overly complex natural and human-made systems. For instance, hurricane simulation has been used to predict hurricane paths and to make a decisions such as evacuation plans. Traffic simulation have enabled us to forecast traffic congestion in cities around the world.

Evaluation and Analysis of Traffic Flow at Signalized Intersections in Nicosia Using of SIDRA 5 Software

Traffic congestion on road networks and signalized intersections have posed significant problems worldwide. One of the significant ways to reduce traffic congestion in cities is by improving the public transportation system. Therefore, it is essential to use advanced software tools to ensure that the current system can be controlled and evaluated. This study was aimed at assessing and analysing the performance of traffic flow at signalized intersections and roundabouts at peak hours in the city of Nicosia (northern part of the island) using the SIDRA INTERSECTION 5 software. It was also aimed at comparing the performance of traffic flow during the morning and evening peak hours at four intersections and two roundabouts. The parameters used to assess the performance of the traffic flow were the level of service, delays and delayed travel speed, performance index, operating cost, fuel consumption, and carbon dioxide emission. It was found that the SIDRA INTERSECTION software was able to give an estimate of the current situation of traffic flow in the city. The results showed that the level of service was low, resulting in low speeds and lots of delays during the evening and morning peak hours. The delay was up to 9318.9 seconds and the fuel consumption was nearly 1431.6 lit/h, while the CO2 emission was up to 3594.7 kg/h.

The study on create transit metropolis promotion mechanism and model-Guiyang case

The sharply develop for urbanization has caused the emergence of urban congestion problem, however, this emergence issue is hampering the process of urban development. Therefore, government gives priority to the development of transit metropolis as an important means to improve the level of unban public transport services, meet basic travel demands for people, as well as, alleviate city traffic congestion and the pressure of the resource issues will surely play an significant roles in new urbanization strategies and building livable cities. On the one hand, this report introduces the transit metropolis, as well as, summaries some experiences of creation and exploit the transit metropolis in overseas countries. On the other hand, it takes Guiyang City as a case study, through research of the progress for creating and promoting transit metropolis to reflect the experience and present existing problems. This provides a strong guarantee and strong support for setting up the national model city of ‘transit metropolises.

Introducing the new paradigm of Social Dispersed Computing: Applications, Technologies and Challenges

Abstract If last decade viewed computational services as a utilitythen surely this decade has transformed computation into a commodity. Computation is now progressively integrated into the physical networks in a seamless way that enables cyber-physical systems (CPS) and the Internet of Things (IoT) meet their latency requirements. Similar to the concept of “platform as a service” or “software as a service”, both cloudlets and fog computing have found their own use cases. Edge devices (that we call end or user devices for disambiguation) play the role of personal computers, dedicated to a user and to a set of correlated applications. In this new scenario, the boundaries between the network node, the sensor, and the actuator are blurring, driven primarily by the computation power of IoT nodes like single board computers and the smartphones. The bigger data generated in this type of networks needs clever, scalable, and possibly decentralized computing solutions that can scale independently as required. Any node can be seen as part of a graph, with the capacity to serve as a computing or network router node, or both. Complex applications can possibly be distributed over this graph or network of nodes to improve the overall performance like the amount of data processed over time. In this paper, we identify this new computing paradigm that we call Social Dispersed Computing, analyzing key themes in it that includes a new outlook on its relation to agent based applications. We architect this new paradigm by providing supportive application examples that include next generation electrical energy distribution networks, next generation mobility services for transportation, and applications for distributed analysis and identification of non-recurring traffic congestion in cities. The paper analyzes the existing computing paradigms (e.g., cloud, fog, edge, mobile edge, social, etc.), solving the ambiguity of their definitions; and analyzes and discusses the relevant foundational software technologies, the remaining challenges, and research opportunities.