Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic accidents.Various congestion pricing and electric vehicle charging policies have been introduced in recent years.Nonetheless,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system.In this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment.The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations.We propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees.The proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection stations.Through large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

Modeling of Artificial Intelligence Based Traffic Flow Prediction with Weather Conditions

Short-term traffic flow prediction (TFP) is an important area inintelligent transportation system (ITS), which is used to reduce traffic congestion. But the avail of traffic flow data with temporal features and periodicfeatures are susceptible to weather conditions, making TFP a challengingissue. TFP process are significantly influenced by several factors like accidentand weather. Particularly, the inclement weather conditions may have anextreme impact on travel time and traffic flow. Since most of the existing TFPtechniques do not consider the impact of weather conditions on the TF, it isneeded to develop effective TFP with the consideration of extreme weatherconditions. In this view, this paper designs an artificial intelligence based TFPwith weather conditions (AITFP-WC) for smart cities. The goal of the AITFPWC model is to enhance the performance of the TFP model with the inclusionof weather related conditions. The proposed AITFP-WC technique includesElman neural network (ENN) model to predict the flow of traffic in smartcities. Besides, tunicate swarm algorithm with feed forward neural networks(TSA-FFNN) model is employed for the weather and periodicity analysis. Atlast, a fusion of TFP and WPA processes takes place using the FFNN modelto determine the final prediction output. In order to assess the enhancedpredictive outcome of the AITFP-WC model, an extensive simulation analysisis carried out. The experimental values highlighted the enhanced performanceof the AITFP-WC technique over the recent state of art methods.

Mathematical Modeling,Field Calibration and Numerical Simulation of Low-Speed Mixed Traffic Flow in Cities

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Cell transmission model based traffic signal timing in oversaturated conditions

In order to investigate enhancements to cell transmission model (CTM) as a tool for traffic signal timing in oversaturated conditions, randomly distributed saturation flow rates and arrival rates were used instead of constant values to simulate traffic flow movement, estimate the average delay of the network and search for an optimal traffic signal timing plan. A case study was given to demonstrate that the proposed methodology can capture unique phenomena in oversaturated conditions such as forward wave, spillback and lane entrance blockage. The results show that CTM underestimates travel time by 25% when compared to Simtraffic, while the enhanced CTM underestimates by only 3%. A second case study shows that a dynamic signal timing plan is superior to a fixed signal timing plan in the term of average delay.

Measurement and influencing factors of urban traffic ecological resilience in developing countries: A case study of 31 Chinese cities

An urban traffic ecosystem is a spatial structure composed of air,population,vehicles,roads,green spaces,and regions.Traffic ecological resilience is a critical issue in high-quality urban development.From the perspective of system optimization,it is important to study the level of urban traffic ecological resilience and analyze its influencing factors.In this study,we evaluated traffic ecological resilience,characterized its spatio-temporal differentiation,and explored its influencing factors by constructing a system of urban traffic ecological resilience and by analyzing the environmental protection and urban construction data in 31 Chinese cities during 2011-2018.By conducting Kernel density analysis,standard deviation ellipse,comprehensive weight determination,panel data regression analysis,andχ2test,we found that traffic ecological resilience was low on the whole and exhibited the temporal trend of“decreasing first and then increasing”and the spatial characteristic of“high in the east,second in the middle,and low in the west”.The cities with high traffic ecological resilience density values were located in Southeast China and tended to move from northwest to southeast.Governance capability,market activity,technological innovation capability,opening degree,and financial resources had significant effects on urban traffic ecological resilience.Finally,we gave some suggestions for improving the urban traffic ecological resilience in Chinese cities as well as other developing countries in the world.

Countermeasures for traffic accidents due to road conditions in China

Regarding the postulate of traffic infrastructure and vehicles, much attention should be given to the effect of road conditions on accidents. With large numbers of traffic accidents on Shenda Freeway, Liaoning Province, Harbin City and others in P. R. China, parameters and the effect of accidents caused by horizontal alignment, vertical alignment, cross section and intersection are studied systematically The disciplinary analysis of these effects are presented in this paper. The viewpoint is acknowledged that high sub grade and steep slopes are against traffic safety, which is common and ignored in high-usage highways in China. Design parameters of the current design criteria and the corresponding countermeasures are suggested for safety on our highways.

KSUTraffic: A Microscopic Traffic Simulator for Traffic Planning in Smart Cities

Simulation is a powerful tool for improving,evaluating and analyzing the performance of new and existing systems.Traffic simulators provide tools for studying transportation systems in smart cities as they describe the evolution of traffic to the highest level of detail.There are many types of traffic simulators that allow simulating traffic in modern cities.The most popular traffic simulation approach is the microscopic traffic simulation because of its ability to model traffic in a realistic manner.In many cities of Saudi Arabia,traffic management represents a major challenge as a result of expansion in traffic demands and increasing number of incidents.Unfortunately,employing simulation to provide effective traffic management for local scenarios in Saudi Arabia is limited to a number of commercial products in both public and private sectors.Commercial simulators are usually expensive,closed source and inflexible as they allow limited functionalities.In this project,we developed a local traffic simulator“KSUtraffic”for traffic modeling,planning and analysis with respect to different traffic control strategies and considerations.We modeled information specified by GIS and real traffic data.Furthermore,we designed experiments that manipulate simulation parameters and the underlying area.KSUTraffic visualizes traffic and provides statistical results on the simulated traffic which would help to improve traffic management and efficiency.

Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

Intelligent Transportation System(ITS)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic quality.With the help of big data and communication technologies,ITS offers real-time investigation and highly-effective traffic management.Traffic Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past data.Neural Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of time.Deep Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction tasks.With this motivation,the current study introduces a novel Slime Mould Optimization(SMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(SMOBGRU-TP)in smart cities.Initially,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization approach.Besides,BiGRUmodel is employed for effective forecasting of traffic in smart cities.Moreover,the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU method.The proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

Optimizing Traffic Signals in Smart Cities Based on Genetic Algorithm

Current traffic signals in Jordan suffer from severe congestion due to many factors,such as the considerable increase in the number of vehicles and the use of fixed timers,which still control existing traffic signals.This condition affects travel demand on the streets of Jordan.This study aims to improve an intelligent road traffic management system(IRTMS)derived from the human community-based genetic algorithm(HCBGA)to mitigate traffic signal congestion in Amman,Jordan’s capital city.The parameters considered for IRTMS are total time and waiting time,and fixed timers are still used for control.By contrast,the enhanced system,called enhanced-IRTMS(E-IRTMS),considers additional important parameters,namely,the speed performance index(SPI),speed reduction index(SRI),road congestion index(R i),and congestion period,to enhance IRTMS decision.A significant reduction in congestion period was measured using E-IRTMS,improving by 13% compared with that measured using IRTMS.Meanwhile,the IRTMS result surpasses that of the current traffic signal system by approximately 83%.This finding demonstrates that the E-IRTMS based on HCBGA and with unfixed timers achieves shorter congestion period in terms of SPI,SRI,and R_(i) compared with IRTMS.

Improving Traffic Conditions by Using Neural Networks

The paper promotes the implementation of a neural network approach to improve one of the most transcendent traffic conditions: the mobility of the cars in any particular junction. Neural networks have proven to be an effective paradigm of modern computing, providing extensive benefits in a wide range of applications. In this sense, the paper uses a BPNN (backpropagation neural network) model. The three input nodes are related to: n1: the amount of cars in the road; n2: the green light interval; and n3:the distance (taking into account the quantity of cars) between the first car in the intersection and the last car in the longest line in front of it. In particular, the paper promotes that each traffic light signal will be capable of offering a new green light interval according to the requirement and constrains of the vitality, ensuring a vehicular mobility level greater than 65%. To assess this idea, the paper presents two experiments confronting the real world data versus experimental results. For example, in the first experiment, the BPNN improves the performance of the real data about vehicular mobility in almost 30%. Finally, some conclusions and future work are presented.

Traffic Conflict Technique Development for Traffic Safety Evaluation under Mixed Traffic Conditions of Developing Countries

In developing countries, the numbers of traffic accidents, injuries and fatalities are very high and tend to increase atsignalized intersections. For example, in Ho Chi Minh City （HCMC） of Vietnam, the number of accidents at signalized intersectionsaccounted for 45% of the total accidents at all the intersections. This fact leads to strong necessity for analyzing traffic safety atsignalized intersections. Nevertheless, the historical accident data in HCMC is not available for deep analysis, this study uses videocameras to capture and analyze conflicts that potentially lead to accidents using TCT （traffic conflict technique）. Conflict severityidentification is one of the most significant steps to evaluate traffic safety at signalized intersections using TCT. Six zones （seriousconflict, common conflict, non-conflict, highest potential serious conflict, potential serious conflict, and potential common conflict） areexplored in this study to clarify conflict severity. This result is based on being the cut off value between serious conflicts and commonconflicts, according to 85% cumulative frequency of TTC （time to collision） and CS （conflict speed） under 3,050 samples size whichwere observed at 10 signalized intersections during August-November, 2014. Such a deep understanding is a scientific basis to studyhow to apply TCT to evaluate traffic safety at signalized intersections under mixed traffic conditions.

Traffic condition estimation with pre-selection space time model

A pre-selection space time model was proposed to estimate the traffic condition at poor-data-detector,especially non-detector locations.The space time model is better to integrate the spatial and temporal information comprehensibly.Firstly,the influencing factors of the "cause nodes" were studied,and then the pre-selection "cause nodes" procedure which utilizes the Pearson correlation coefficient to evaluate the relevancy of the traffic data was introduced.Finally,only the most relevant data were collected to compose the space time model.The experimental results with the actual data demonstrate that the model performs better than other three models.

Effects of Periodic Boundary Condition on Traffic Waves

In this paper, we use the speed-gradient model proposed by Jiang et al. [Transp. Res. B 36 （2002） 405] to study the effect of boundary condition on shock and rarefaction wave. Our numerical results show that this model can reproduce the evolution of the two traffic waves, which further proves that this model can be used to perfectly explore the consequences caused by various boundary conditions.

Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism

In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.

Characteristics and Meteorological Conditions of Ozone Pollution in Shantou City

Based on routine monitoring data of air quality and meteorological data in Shantou City during 2015-2019,temporal-spatial variation characteristics of O 3 pollution and its correlation with meteorological conditions in Shantou City were explored.The research results showed that O 3 pollution days in Shantou City showed an increasing trend year by year,and O 3 pollution had far-distance transportation and the development trend from offshore Nan ao Island to urban district.In spring and autumn,there was serious O 3 pollution,and it was the most prominent in October.Its diurnal variation in O 3 pollution days was mainly wide-peak type in the afternoon,showing as that O 3 concentration declined slowly after the noon.In O 3 pollution days,higher O 3 concentration was easy to appear at night,causing that O 3 peak in the second day was uplifted,and there was continuous O 3 pollution.Combining backward trajectory analysis chart,it was found that Shantou was mainly affected by coastal transport of northerly polluted air mass,and it was transported into Shantou City from the east to the northeast.O 3 from long-distance transmission superimposed with locally generated O 3,which commonly pushed up the level of O 3 concentration.The weather of O 3 pollution in Shantou City had the characteristics of high temperature and low humidity.There was 25-30℃of temperature interval and 46%-60%of relative humidity interval,and it was accompanied by grade-2 easterly wind.

Potential Application of iRap for Road Safety Assessment under Mixed Traffic Condition Case Study: Le Van Viet Street in Ho Chi Minh City

Road transport safety policies have emphasized road infrastructure safety design and engineering as a core function.However,in developing countries like Vietnam,this approach has been slower to adopt,resulting in substandard roads.In-depth studies of accident locations indicate that road environment factors contribute significantly to road accidents in Vietnam and road design features are associated with specific accident types and hazards.Proactive and reactive approaches,such as road safety audit,inspection,assessment,and treatment of hazardous locations,are necessary to ensure that the road and its environment are safe.This paper provides an overview of road safety in Vietnam in general,and Ho Chi Minh in particular,including its factors and characteristics,as well as road infrastructure safety improvements.The iRap tool for road safety inspection and assessment is highlighted as a potential method for systematically analyzing road infrastructure deficiencies and providing targeted countermeasures to improve road safety under mixed traffic conditions.

Traffic Control Based on Integrated Kalman Filtering and Adaptive Quantized Q-Learning Framework for Internet of Vehicles

Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.

Urban Accessibility in Motorcycle Dependent Cities - Case Study in Ho Chi Minh City, Vietnam

Motorcycle dependent cities have specific characteristics in terms of urban accessibility.A rapid increase in the number of motorcycles and other private motorized modes make transport problems more serious and cause severe capacity problems for the infrastructure systems in these cities.Therefore,it is necessary to optimize the development of different modes to meet travel demand and to ensure accessibility in all urban areas.This paper aims to explore accessibility conditions in Ho Chi Minh City,a typical motorcycle dependent city in Vietnam.Understanding of accessibility could be the key element for urban planning in Ho Chi Minh City in particular and motorcycle dependent cities in general.Then,management measures for motorcycles and competitive modes will be proposed to improve the accessibility conditions and thus support sustainable urban transport development for motorcycle dependent cities.

Modeling for Action of Recovering from Erroneous Driving Condition Based on Revised Decision Tree

Driving behavior modeling is very important in the research area of road traffic systems safety analysis. The characteristics of action of recovering from erroneous driving condition underlying road traffic accident or incident scenarios is quantitatively analyzed, the model of action of recovering from erroneous driving condition is set up according to the identification of erroneous driving condition and the measurement of correction from erroneous driving condition. And then, the probability of action of recovering from erroneous driving condition has been measured based on a revised decision tree. The measure process uses a combination of test data and subjective judgments of driving behavior. It can provide a very helpful theoretical basis for the further analysis of driving behavior in road traffic system.

交旅融合背景下城市快进慢游体系的规划与研讨

交旅融合是交通运输和旅游服务结合形成的一种新型产业模式,推动传统交通由单一通达功能向游憩、文化、经济、生态和社会等多功能转型突破。以南京市主城区为研究对象,在分析研究区交旅融合发展的基础上,构建域外快进、域内慢游的“快进慢游”城市交旅融合发展体系,对促进城市旅游业和交通业高质量发展均具有重要的理论与现实意义。