Intelligent Transportation System and Night Delivery Schemes for City Logistics

Intelligent Transportation System （ITS） integrates information and communication technologies with location based technologies into roads, vehicles, traffic and transport management systems. Application of ITS can improve situation in major cities where due to the increasing number of residents and level of motorization traffic congestion represents important issue. Another problem arises from this facts and that is that cities have contradictory needs as grooving need for goods within urban areas as well as need for less vehicles in the same area. The urban logistics activities by private companies within urban areas represent an integral part of city logistics that aims to improve the efficiency of urban freight transportation, reduce traffic congestion, mitigate environmental impacts, reduce costs and fuel consumption. This research presents application of ITS technologies and vehicle routing problem for night delivery scheme planning as one of the core techniques for modelling city logistics.

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.

Design and Analysis of Traffic Information Transmission System Based on Visible Light Communication

Visible Light Communication( VLC) based on LED is a new wireless communication technology with high response rate and good modulation characteristics in the wavelengths of 380- 780 nm. Compared with conventional methods,the waveband of VLC is harmless to human and safe to communication because of no magnetism radiation. An audio information transmission system using LED traffic lights is presented based on VLC technology. The system is consisted of transmitting terminal,receiving terminal and communication channel. Some experiments were made under real communication environment. The experimental results showed that the traffic information transmission system works steadily with good communication quality and achieves the purpose of transmitting audio information through LED traffic lights,with a data transfer rate up to 250 kbps over a distance of 5 meters.

A Framework for Intelligent Decision Support System for Traffic Congestion Management System

Traffic congestion problem is one of the major problems that face many transportation decision makers for urban areas. The problem has many impacts on social, economical and development aspects of urban areas. Hence the solution to this problem is not straight forward. It requires a lot of effort, expertise, time and cost that sometime are not available. Most of the existing transportation planning software, specially the most advanced ones, requires personnel with lots practical transportation planning experience and with high level of education and training. In this paper we propose a comprehensive framework for an Intelligent Decision Support System (IDSS) for Traffic Congestion Management System that utilizes a state of the art transportation network equilibrium modeling and providing an easy to use GIS-based interaction environment. The developed IDSS reduces the dependability on the expertise and level of education of the transportation planners, transportation engineers, or any transportation decision makers.

An Optimal Deep Learning for Cooperative Intelligent Transportation System

Cooperative Intelligent Transport System(C-ITS)plays a vital role in the future road traffic management system.A vital element of C-ITS comprises vehicles,road side units,and traffic command centers,which produce a massive quantity of data comprising both mobility and service-related data.For the extraction of meaningful and related details out of the generated data,data science acts as an essential part of the upcoming C-ITS applications.At the same time,prediction of short-term traffic flow is highly essential to manage the traffic accurately.Due to the rapid increase in the amount of traffic data,deep learning(DL)models are widely employed,which uses a non-parametric approach for dealing with traffic flow forecasting.This paper focuses on the design of intelligent deep learning based short-termtraffic flow prediction(IDL-STFLP)model for C-ITS that assists the people in various ways,namely optimization of signal timing by traffic signal controllers,travelers being able to adapt and alter their routes,and so on.The presented IDLSTFLP model operates on two main stages namely vehicle counting and traffic flow prediction.The IDL-STFLP model employs the Fully Convolutional Redundant Counting(FCRC)based vehicle count process.In addition,deep belief network(DBN)model is applied for the prediction of short-term traffic flow.To further improve the performance of the DBN in traffic flow prediction,it will be optimized by Quantum-behaved bat algorithm(QBA)which optimizes the tunable parameters of DBN.Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flowin real-time with amaximumperformance under dissimilar environmental situations.

Design and analysis of traffic incident detection based on random forest

In order to avoid the noise and over fitting and further improve the limited classification performance of the real decision tree, a traffic incident detection method based on the random forest algorithm is presented. From the perspective of classification strength and correlation, three experiments are performed to investigate the potential application of random forest to traffic incident detection： comparison with a different number of decision trees; comparison with different decision trees; comparison with the neural network. The real traffic data of the 1-880 database is used in the experiments. The detection performance is evaluated by the common criteria including the detection rate, the false alarm rate, the mean time to detection, the classification rate and the area under the curve of the receiver operating characteristic （ROC）. The experimental results indicate that the model based on random forest can improve the decision rate, reduce the testing time, and obtain a higher classification rate. Meanwhile, it is competitive compared with multi-layer feed forward neural networks （MLF）.

Building trust for traffic flow forecasting components in intelligent transportation systems via interpretable ensemble learning

Traffic flow forecasting constitutes a crucial component of intelligent transportation systems(ITSs).Numerous studies have been conducted for traffic flow forecasting during the past decades.However,most existing studies have concentrated on developing advanced algorithms or models to attain state-of-the-art forecasting accuracy.For real-world ITS applications,the interpretability of the developed models is extremely important but has largely been ignored.This study presents an interpretable traffic flow forecasting framework based on popular tree-ensemble algorithms.The framework comprises multiple key components integrated into a highly flexible and customizable multi-stage pipeline,enabling the seamless incorporation of various algorithms and tools.To evaluate the effectiveness of the framework,the developed tree-ensemble models and another three typical categories of baseline models,including statistical time series,shallow learning,and deep learning,were compared on three datasets collected from different types of roads(i.e.,arterial,expressway,and freeway).Further,the study delves into an in-depth interpretability analysis of the most competitive tree-ensemble models using six categories of interpretable machine learning methods.Experimental results highlight the potential of the proposed framework.The tree-ensemble models developed within this framework achieve competitive accuracy while maintaining high inference efficiency similar to statistical time series and shallow learning models.Meanwhile,these tree-ensemble models offer interpretability from multiple perspectives via interpretable machine-learning techniques.The proposed framework is anticipated to provide reliable and trustworthy decision support across various ITS applications.

CHRT:Clustering-Based Hybrid Re-Routing System for Traffic Congestion Avoidance

Re-routing system has become an important technology to improve traffic efficiency.The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic,making the planned routes unable to cope with the changing traf-fic conditions.Based on real-time traffic information,it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion.Moreover,how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system.To deal with these challenges,this paper proposes CHRT,a clustering-based hybrid re-routing system for traffic congestion avoidance.CHRT develops a multi-layer hybrid architecture.The central server accesses the global view of traffic,and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead.Then,a clustering-based priority mechanism is proposed,which sets priorities for clusters based on realtime traffic information to avoid secondary congestion.Furthermore,to plan the optimal routes for vehicles while alleviating global traffic congestion,this paper presents a multi-metric re-routing algorithm.Through extensive simulations based on the SUMO traffic simulator,CHRT reduces vehicle traveling time,fuel consumption,and CO2 emissions compared to other systems.In addition,CHRT globally alleviates traffic congestion and improves traffic efficiency.

Developing architecture of a traveler information system for dynamic equilibrium in traffic networks

Having criticized the current architecture of Advanced Traveler Information Systems （ATISs）, this work discusses a new base of requirements to develop a new paradigm for traffic information systems. It mainly integrates three dimensions within a traffic system： drivers＇ pattern of behavior and preferences, urban traffic desires, and capabilities of traffic information service providers. Based on the above, functional segments from several related backgrounds are brought together to structure a new architecture, called Interactive Traveler Information System （ITIS）. The main interactive feature of this new architecture is a two-way communication track between drivers and the traffic information system provider-in fact, a decision on choosing a road at a particular time for an individual will be made based on the utility of both sides. This new configuration consists of driver-side smartphone application, centric traffic prediction, and decision-maker units, which will shape a new approach of decision-making processes. These all work together to satisfy the designated goal of ITIS, which is preserving the Wardrop equilibrium condition in the traffic network level. Finally, we concentrate on a comparison study, which shows a differentiation between performance of the proposed ITIS and the current ATIS model in a real situation. This has been done with simulations of analogical scenarios. The most noticeable advantage of the proposed architecture is not being limited to a saturation limit, and the positive effect of increasing system penetration in the performance of the newly introduced information system. In conclusion, new research subjects are suggested to be carried out.

A two-stage short-term traffic flow prediction method based on AVL and AKNN techniques

Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.

Quantifying the Indirect Benefits of Transport Infrastructures and Value Capture Ways in Transportation

Urban transportation is closely related to land use. The changes of transportation cost and accessibility have a direct impact on land use and land prices. Land exploitation and land use play a role of redistributing traffic flow in the urban districts. Real property values are regarded as a sole comprehensive index representing all the benefits generated by transport investment. We have applied the theory of time value on transportation to land price model to evaluate the increased value of real estate resulting from improved accessibility. Agency responsible for the development of urban transport infrastructure can capture part of the financial benefit gained by real estate development through a variety of value capture. This enables us to transfer indirect benefit of transport infrastructure construction to transport enterprise and achieve a benign cycle between urban transport investment and land exploitation.

Traffic dynamical characteristics and nonlinear density waves with consideration of navigation

In this dissertation, based on the existing macroscopic and microscopic models for traffic flow, several improved mathematical models in accordance with the rapid development of intelligent transportation system （ITS） are proposed, and the corresponding theoretical analysis and numerical simulation are performed. In addition, the relationships between macroscopic and microscopic traffic models are examined, in which emphasis is particularly laid on the investigation of the various nonlinear density waves in traffic flows. The contents of the dissertation are as follows.

Research on an Urban Traffic Control System Based on DGPS

The basic principles of GPS (Global Positioning System) and DGPS (Differential GPS) are described. The principle and structure of vehicle navigation systems, and its application to the urban traffic flow guidance are analyzed. Then, an area coordinated adaptive control system based on DGPS and a traffic flow guidance information system based on DGPS are put forward, and their working principles and functions are researched. This is to provides a new way for the development of urban road traffic control systems.

An Urban Traffic Monitoring System Based on the ArcGIS Engine

The growing number of vehicles makes traffic jams and accidents significant problems. Making people get to know the real-time road condition can mitigate the effect of congestions greatly, but this is not supported by traditional traffic assistant systems. The intelligent traffic system is born to settle these problems. By making full use of the ArcGIS （Arc Geographic Information System） Engine characteristics, this paper designs and imple- ments an urban traffic monitoring system. The main functions of the system include the real-time road condition information display, layer-control, supervisory control management and the basic operations of a map. With the data collected by monitors deployed in intersections, different road conditions are calculated and shown with dif- ferent colors on the map and users can choose suitable roads to get away from the traffic congestion; meanwhile it can offer a reference for a traffic management department to make decisions on traffic control. The system has been deployed and shows high practicability and reliability in practical use.

An integrated and cooperative architecture for multi-intersection traffic signal control

Traffic signal control(TSC)systems are one essential component in intelligent transport systems.However,relevant studies are usually independent of the urban traffic simulation environment,collaborative TSC algorithms and traffic signal communication.In this paper,we propose(1)an integrated and cooperative Internet-of-Things architecture,namely General City Traffic Computing System(GCTCS),which simultaneously leverages an urban traffic simulation environment,TSC algorithms,and traffic signal communication;and(2)a general multi-agent reinforcement learning algorithm,namely General-MARL,considering cooperation and communication between traffic lights for multi-intersection TSC.In experiments,we demonstrate that the integrated and cooperative architecture of GCTCS is much closer to the real-life traffic environment.The General-MARL increases the average movement speed of vehicles in traffic by 23.2%while decreases the network latency by 11.7%.

Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

Traffic flow prediction plays a key role in the construction of intelligent transportation system.However,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very challenging.Most of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between nodes.However,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial features.This paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic flow.By combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic data.Experiments on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.

Smart Parking Management System “MOTAH”

With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerbated these challenges. This pressing issue underscores the critical necessity for a structured approach to managing university entries and overseeing parking at the gates. The proposed smart parking management system aims to address these concerns by introducing a design concept that restricts unauthorized access and provides exclusive parking privileges to authorized users. Through image processing, the system identifies available parking spaces, relaying real-time information to users via a mobile application. This comprehensive solution also generates detailed reports (daily, weekly, and monthly), aiding university safety authorities in future gate management decisions.

Analysis of the Application of Artificial Intelligence in Transportation

With the advancement of the information age,the transportation industry has experienced rapid growth,leading to an expansion in the scale and number of highway constructions.However,this development has also given rise to numerous traffic issues,including frequent vehicle congestion and traffic accidents.To address these problems,it is essential to leverage modern technology for real-time information collection and analysis,providing robust technical support for intelligent transportation systems.This paper focuses on artificial intelligence(AI)technology,explaining its concept and its role in intelligent transportation.It reviews the various application areas and analyzes the use of AI in intelligent transportation.Finally,it proposes strategies for applying AI to promote the healthy development of intelligent transportation systems.

基于Android平台的室内停车场智能寻车系统研究

为了解决车主在大型室内停车场找车的难题,提出了一种室内停车场反向寻车系统技术方案。采用Android智能手机作为信息采集和处理终端,结合二维码扫描定位技术、惯性传感器的室内导航技术、ArcGIS地图服务和离线地图包技术,ArcGIS API for Android插件技术实现了车辆精确定位、反向查找、实时路径引导等功能。并设计实现了软件系统,在某大型室内停车场进行了实施验证,结果证明了技术方案的可行性。