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Non-motorised traffic plays an important and unique role in an efficient traffic system. Helping the creation of more available multimodal communities, improvements in non motorised traffic can lead to a decrease of the use of motorised vehicles and special attention should be paid to improvements in non motorised traffic which are integrated to policies of racional usage of terrain. Conventional research often underestimates and disregards the importance of walking and bicycling, which may present a problem during traffic planning and projecting. It's important to estimate the benefits and costs which improvements in non motorised traffic bring with them. That is why the paper analyzes the impact of non motorised traffic to congestion and safety of traffic, preservation of energy, decrease of pollution, health and social inclusivity of population and economic development.

Ancient Chinese military castles were equipped with rapid transportation routes for mutual aid, and this was an essential indicator of regional defense capability. However, since the sites of these transportation routes have mostly disappeared, it is not easy to examine the actual distribution of these routes. It is necessary to speculate the trend and position of military routes on the basis of the castle locations. In this study, the geographic features of each castle location were extracted as factors affecting the efficiency of the intercastle transportation system using the ArcGIS cost path function. By analyzing the fit of each factor for screening and weight assignment, a time cost path was established, and a model was generated for calculating the efficiency of this transportation system. The Weihai area, a typical representation of sea defense during the Chinese Ming Dynasty, was taken as an example for simulation. Overall, five ancient military transportation routes were restored. The establishment of the Ming Dynasty Wendengying transformed the linear defense layout of the Weihai region into a longitudinal network layout, and its site selection was of great benefit to the overall defense of the coastal citadel of Weihai. This model breaks the traditional limitations of relying on subjective speculation for ancient road restoration and dramatically improves its accuracy and credibility. Moreover, it makes a significant contribution to judging the road systems of ancient cities in different regions and provides a new idea to quantify the efficiency of ancient castle defenses.

Knowing the regulations – static and dynamic – is, however, nowhere near solved. This chapter discusses two major problems. The first and greatest problem is that most vehicle OEMs have thought, and continue to think, that their vehicle is at the centre of, indeed, is in control of, its movements through any traffic system. The second big problem is knowledge of the regulations at any place and time. This chapter considers Mobility as a Service (MaaS) in two paradigms – MaaS today with existing transport modes (including driver assisted ride hailing) – and MaaS of tomorrow with driverless ride hailing using automated vehicles (AVs). The provision of regulatory data from the TIS, and route optimisation from the travel optimisation service will be vital to the safety of AVs. AVs will become steadily more important, but the take‐up will be slower than many have hoped.

Intelligent Transportation System (ITS) is now being widely built all over the world. Traffic congestion prediction, as a major part of ITS, not only provides reliable traffic information for travelers to save their time, but also assists traffic management agencies to manage the traffic system. We find that the existing papers, including neural networks, do not perform well due to the complex association features between the road sections. In addition, the lack of a better evaluation standard for traffic congestion also makes the effect of traffic congestion prediction worse. In this paper, we propose a method to generate traffic congestion index by mining free-stream speed and free-stream flow. Considering the association features of road segments in the road network, we propose a road segment grouping method based on association subgraph to pre-train deep learning model and realize information sharing between road segments. Combining the features of traffic data and CNN model, we propose a traffic congestion prediction model named SG-CNN which training process is optimized by road segment grouping algorithm. The experiments demonstrate that our proposed model has higher accuracy rate than other models.

The future transportation systems demand an intelligent traffic system that can be achieved by connecting the vehicles to the Internet of Things (IoT) which in turn forms an Internet of Vehicles (I...

In order to reduce the fluctuations of traffic flow and the impacts of traffic flow fluctuations, thereby increasing the level of service of roads and constructing the Internet of Things-based traffic system, based on the various causes of traffic flow fluctuations, the countermeasures were taken, such as the optimization of lanes, the management of intersections, the improvement and perfection of slow-vehicle lanes and bus stops, the timely renewal of traffic signs and marks, the rational arrangements of road operations, and the removal of visual obstacles, etc. The research results showed that these countermeasures could effectively reduce the hidden peril of accidents and improve the road capacity, thereby improving the level of service of roads, which provided a theoretical foundation and basis for road engineering design and traffic management. It can be seen that improving urban traffic through rational management and control measures can effectively improve the level of service of urban traffic.

The increase of safety for the vulnerable road users (VRUs) can be managed by an effective traffic system that implies users’ interactions through a smart application. This paper proposes an interactive traffic application based on the strong connectivity of all traffic participants and with the smart infrastructure. Thus, the proposed architectural solution is based on two main components: one part running on the user equipment (UE) with a mobile application that has an intuitive interface, and the other one running in the backbone network, where the pieces of information are collected from different sensors, fused and processed in a proper amount of time.

The Vessel Traffic Services is a control system in the maritime traffic system where the VTS operators need to cope with a complex environment to contribute to safe and efficient ship movements. By...

Driverless car, as a direction for future automobile development, greatly improves the efficiency and safety of the traffic system. It’s one of the most popular technical fields. In recent years, driverless car has developed rapidly. The related development is concerned by governments, businesses, consumers and stakeholders widely, and most of countries have been actively studying this technology. This paper first introduces the current development of driverless car at home and abroad. Besides, the basic technologies of driverless car are briefly analyzed. In addition, the author compares the American government’s attitudes with Chinese government’s attitudes towards driverless car. Specifically, the article makes an analysis of contents of literature and periodicals at home and abroad and policies and documents which have already been published. The analysis shows that there is no great difference between the attitudes of Chinese and American governments. Both of two governments actively support the development of driverless car. Finally, this paper expounds the development direction of the driverless car field in future by dividing into two categories through road conditions: automatic driving on expressways and automatic driving in cities.

Currently, car manufacturers, suppliers, and IT companies are surpassing each other with ambitious plans regarding their driving automation technology. However, even the most optimistic announcements grant that, for a certain time, a human driver cannot be replaced in all driving situations. Hence, human drivers will still be a part of future traffic by working together with automation systems. Analyzing the joint decision-making process of such a human–machine system in automated driving provides a better understanding of the resulting traffic system. In this paper, a driving simulator study with 33 participants focusing on the utility of cooperative driver–vehicle systems with the use case of highway driving is presented. Based on the study’s results, a model that explains the linkage between subjective measures such as the perceived utility and objective driving data is derived. Moreover, on an individual level, models are parameterized by using driving states as predictors and the individual utility perceived in a driving situation as response. This individual utility can be used for predicting driving actions such as the initiation of overtaking maneuvers.

Planning a successful metro railway system is challenging due to the fast development of urban cities and time-consuming construction. Recently big data reflecting city dynamics has become widely available, which enables us to solve this challenging problem from a data mining perspective. In this paper, we propose a framework to evaluate the traffic efficiency of metro railway systems from various factors such as the railway traffic flow, the structure of the traffic system and the spatial distribution of work-and-home. Based on the commute data (e.g., railway boarding) of Shenzhen rail transit reported by 28,000 passengers and electronic boarding card data provided by Shenzhen Railway Company, we assess the bottlenecks and congested areas of the system, understand passenger travel patterns, and observe organizational operations and deficiencies. The experimental results help us better understand how big data can help make Shenzhen metro railway more efficient and effective in terms of planning and management in the future.

With the rapid development of technology, the Internet of Things (IoT) has been associated with various fields, such as smart home networks, intelligent traffic system (ITS), and so on. Vehicular ad-hoc networks (VANETs) can provide value-added services by roadside units (RSUs) for vehicles. Car-home connectivity can be defined as allowing drivers and passengers to remotely control smart appliances in the smart home while driving. The communication process between connected vehicles and smart appliances may cause personal information disclosure. To protect the security and privacy of communication, this paper proposes an authentication scheme for car-home connectivity services. The proposed scheme can protect the security and privacy of the communication process between the vehicle and the smart appliances and multiple third trusted authorities can cooperate to retrieve the identity of misbehaving users. The system analysis shows that the proposed scheme is efficient, secure, privacy-preserving, and easily deployable.

Based on the LIM(Linear Induction Motor) track traffic system of Guangzhou, vertical dynamic coupling model of LIM vehicle-concrete slab track and their dynamic differential equations are set up, including the model of vehicle, the model of vehicle-track interaction, the model of the rail and the concrete slab track. The control technology of gap between LIM and reaction-plate and the vibration characteristics of the concrete slab track are analyzed by the dynamics simulation testing. Reasonable suggestions are put forward for the design, construction and maintenance of the slab track and the track traffic system of LIM.

Automatic traffic scheduling can improve the efficiency of the traffic system by decreasing significant delays and easing congestion. This paper introduces a machine learning (ML) based algorithm to automate the traffic scheduling based on the density of vehicles waiting, without any human intervention. Presently, the traffic signal timers are preset which is unreasonable for a stochastic process like vehicular traffic. The proposed methodology detects vehicles with piezoelectric sensors embedded across each lane. The two-point time ratio method is utilized to identify the vehicles using the sensor's data and the vehicles are classified based on their pick-up speed. Further, a TinyML based model is proposed to predict the green signal timings.

Transportation is a field of activity that is very important in people’s lives. The importance of transportation for the community is caused by many factors, one of which is the geographical condition of an area which allows transportation to be carried out by land, sea or air to reach all areas. Transportation is the lifeblood of National Development to smoothen the flow of people, goods and information to support the optimal allocation of economic resources. Therefore, transportation services must be sufficiently available and affordable to the public’s purchasing power. Transportation that is widely used by people in Indonesia is one of the only land transportation. The increase in population and city area causes the amount of traffic to also increase. While the traffic system is nearing saturation, thus increasing the amount of traffic has a major impact on the environment. This study uses a quantitative approach, explanation, and planning that refers to the results in the field and survey observations of operational conditions in existing conditions and after using the Railbus mode (multimodal). The quantitative method is called a positivistic method because it is based on the philosophy of positivism. And in general, transportation plays an important role in terms of economic development and non-economic development Integration between modes has many advantages, one of which is service tariffs which can be reduced through the tariff integration mechanism. Keywords: Integration, Transportation, Economy, Prospects

The development of tourism brings economic benefits as well as additional pressure on the urban traffic system. For example, the travel time of tourists coincides with the rush hour of urban residents’ daily commuting. Limited urban traffic resources cannot meet the travel needs of tourists and urban residents at the same time, resulting in traffic congestion and low travel efficiency. Now, with the development of intelligent technology, tourists can obtain real-time information about transportation systems through various channels and adjust their travel behavior accordingly. This study shows tourists’ travel behavior based on a survey conducted to the tourists in Yangzhou city. 1500-interview data are analyzed, and a Multinomial Logit Model (MNL) was employed to establish the probability prediction model of tourists’ departure time choice. The results presented that sync traffic information and some other tourism-related factors determine the choice of tourists’ departure time. These factors distinguish the travel behavior of tourists from the daily travel behavior of urban residents. This study can provide suggestions for the urban tourism management department to formulate more targeted and efficient policies while creating a more comfortable tourism environment for tourists.

The rapid motorization of China has led to widespread traffic issues. While solutions exist, transport providers can often create new problems while solving another. Currently two choices dominate the public debate; firstly the automobile as a comfortable transport mode that offers the idea of good mobility for the individual, an offering degraded by traffic congestion; and secondly the alternative of high-efficiency public transport which offers the consumer less door to door comfort but moves more people. This chapter investigates opportunities in the third choice of smaller vehicles. Such vehicles take into account efficiencies, comfort, and door to door access, but require both design innovation and regulatory innovation to be a more attractive option for urban commuters in China. We conceive of a traffic system which can balance the high efficiency of public transport with the mobility and comfort of personal vehicles.

Recently, with the enhancement in the field of remote sensing and computation techniques, road detection from satellite images is getting possible. In these days, precise extraction of the lane from satellite images has become one of the major important fields of research in both remote sensing and transportation. The road network performs an imperative role in the traffic system, urban planning, route planning, and self-driving. In this paper, technique for road segmentation from the satellite images has been introduced. In the proposed method, a custom deep neural network (DNN) has been used for the detection of the road from satellite images. We have used a simple and custom neural network which is computationally faster and as accurate as a traditional deep neural network like Inception, YOLO, and ResNet-50 for road detection in the satellite images. In the initial stage, images are preprocessed with the help of OpenCV and morphology. We have annotated each pixel value as 0 for non-lane pixels and 1 for lane pixels. With this annotated data, we have trained our custom DNN model. The road region is denoted by white pixels, and black pixel denotes a non-road region. In the final result, the noise removal technique is used to remove distorted white pixels to improve the accuracy further.

In major cities of Vietnam, serious road congestions are partially caused by rainfalls and road flooding. To help mitigate negative impacts of the adverse weather conditions on transport and traffic system, it is important to implement weather-responsive traffic management strategies for those cities. However, weather-responsive traffic management measures are often formulated based on practitioners’ experience and not tested prior to application. Taking Ho Chi Minh City as a case study, this paper aims to develop a methodological framework to help formulate and test such strategies. It consists of literature review of good practices, expert consultation process, road user interview survey, and strategic travel demand model-based scenario test. Active traffic management strategies are suggested for the city of study and a number of selected measures are tested by a VISUM model. It shows that vehicle speeds and travel times could be improved significantly if weather-responsive traffic management strategies are applied. This research could serve as a toolkit to assist the design of smart urban traffic management system.

In Recent days an intelligent traffic system [ITS] is implemented on indian traffic sytem. Different applications are widely used to improvies the performance of the system. To improve the intelligence of the system deep learning can used to classify the vehicles into three different classes. The combination of Faster RCNN classifier and RPN can used to detect the objects and classify those objects into different classes. Analysis of the experimental results shows the improved accuracy and efficiency in classifying the vehicles on indian roads into different categories.

The evolving problem of traffic problems is an inevitable situation in wide and rising cities worldwide. Population growth has led to a growth in the number of cars leading to absolute congestion development. Life is sacred and can not be repatriated until lost. The reaction time required by the emergency responders plays a vital role in the face of disasters. This might be a department for medical, disaster, or defense. The greatest challenge they face is congestion from flow. To address this, an intelligent traffic system is needed using a preemptive equation that adapts rapidly to environmental conditions. The aim of this article is to identify the emergency call headed back to the location and to monitor the congestion system in order to provide efficient facilities. This journal also sets out a method that uses a Zigbee component and Internet of Things (IoT) to transmit the treatment request from the ambulance to the nearby hospitals, while ambulance attaining the road junction, the smart traffic system which in turn changes the traffic signal cycle. This system can be implemented throughout the city thereby reducing the delay.

In current scenario one of the serious concern for people in smart cities such as Delhi, Bangalore, Mumbai, Hyderabad etc. is traffic congestion. This has turned out to be a daily problem in current time. Due to this road congestion accidents in the city have are raised to a great extent so lives lost due to accidents are even more crucial. Due to this congestion on the roads, emergency vehicles such as ambulances, fire-cars and other vehicles cannot reach on time. This results in huge loss of life. To tackle these issues up to a great extent we present a solution in this paper. Through the use of IoT enabled technology we can solve these issues by creating "Green Corridors" for emergency vehicles. This can save their time expansion for reaching the proposed destination and can prevent the loss of human life up to great extent. IoT provides a method to tackle these issues by providing smart and intelligent traffic management system. The system gives emergency vehicles the benefit of green corridor and reach destination on time. The RFID reader scans the RFID tag applied on the ambulance and updates the upcoming traffic light to switch to green and displays a message to vehicles ahead of ambulance to provide a "Green Corridor" by shifting other lanes. This system comprises whole traffic system and functions for emergency situations in order to reach the destination without any delay.

Due to huge number of private and public vehicles in last two decades, the traffic load and congestion have increased significantly, which is major problem in transportation system. An intelligent traffic management system becomes an important part of the transportation system to manage the traffic properly in smart cities. In this paper, we have proposed intelligent traffic management system to build smart platform in Mandalay, Myanmar. The main aim of the paper is to reduce traffic congestion, road crash accidents, fuel consumption and save travel time. To provide safe, comfortable, and less frustrating travel, this paper uses big data technology and machine learning technique for analysis of the volume of traffic data and predicts optimal road traffic using machine learning.

Shan, S., 2020. Research on government support policies for coastal transportation projects based on DEA-SBM model. In: Yang, D.F. and Wang, H. (eds.), Recent Advances in Marine Geology and Environmental Oceanography. Journal of Coastal Research, Special Issue No. 108, pp. 262–265. Coconut Creek (Florida), ISSN 0749-0208.The transportation system of coastal cities is an important part of the social and economic system of coastal cities. The sustainability of its development is the premise to support the sustainable development of coastal cities. Sustainable development includes the planning, construction, operation, and management of transportation systems in coastal cities. In this paper, the data envelopment analysis (DEA) evaluation model and method of synergy and development efficiency of a coastal city traffic system was put forward from the angle of input and output in the traffic system and each subsystem, the index system of sustainable development of coastal urban traffic system, and then the DEA evaluation model of the coordinated development of the coastal city traffic system.

In the last decade, Bandung has become one of the tourism destination places in Indonesia. It is reported that almost 6.7 million visitors come to Bandung in 2018, and the number increased by almost 4% per year since 2014. This rise in the number of visitors leads to the establishment of several toll gates as main access points throughout the city. As one of the busiest ones, Buah Batu toll gate is frequently congested because of the location that is close to the southern part of Bandung. To overcome this problem, a traffic regulation based on computer simulation is urgently required. In this study, we simulate the traffic system on the Buah Batu toll gate by using a combination of Nagel-Schreckenberg (NaSch) and Daoudia and Moussa (DM) models. NaSch model was used to defined vehicle movement, while the DM model was used to allow a vehicle to change lane. We defined three scenarios to evaluate the effectivity of the closing gate scheme. We found that the closing of gate 5 is more effective than the closing of gate 1. We also investigated the contribution of traffic density and driver’s behavior, e.g., stopping behavior and lane-changing behavior, to the average velocity of the vehicles.

The status quo in navigation systems is to use real-time traffic detection to determine congestion-free paths and routes vehicles accordingly. However, such a service does not communicate with the traffic signals to regulate the traffic flow. As a result, there is a potential for reducing travel time that is not being leveraged. If the navigation system and the traffic system could communicate and were part of the same feedback loop, traffic systems can take decisions based on the destinations of vehicles queued. Furthermore, traffic signals can detect when certain roads are blocked with vehicles, and prioritize clearing of the blocked roads. Yet another benefit of such a system is that ambulances and government vehicles can be given a higher priority. In this paper, such a feedback loop is implemented on a custom-built simulator to analyze if the average travel time of all the vehicles reduces in the system under various traffic conditions.

the purpose of road accidents forecasting is to analyze the tendency of road accidents under existing road traffic conditions, evaluate the feasibility and practical effectiveness of traffic safety measures reasonably, control the factors affecting road accidents, and reduce the traffic accidents. The characteristics such as non-linearity, randomness and uncertainty in traffic system make it difficult to forecast road accidents, the behavioral feature of traffic system. The objectives of this study are to determine the critical accident variables for accident prediction purposes, to utilize Artificial Neural Network (ANN) as a tool for accident prediction analysis, and to develop an Accident Prediction Model. This study uses a series of artificial neural networks to model and estimate accident by the applied software that is NeuroSolutions 6.0. An important implication of this paper after sensitivity analysis is that the variables like mixed-use developments, side-drain condition, guard rail, marker-post, chevron markings and curve warnings, cross-drainage works, over-head structures, Fly-over Bridge, side roads/access roads, under-pass, and volume of traffic are most significant factors which increase accident.

Urban multimodal traffic refers to various modes of trips formed in the process of urban development. The development of cities and urban traffic is interactive. On the one hand, the economic development and technological progress of the city promote the development of urban traffic, on the other hand, the development of urban scale and residents' travel have put forward new demands for urban traffic. Urban multi-mode traffic system is a comprehensive transportation system gradually formed in the process of urban development and urban traffic development, which has the characteristics of precipitation and specialization, slow variability, regional, compatibility, and coordination. The formation of urban multimodal traffic system conforms to the trend of urban development and provides a positive role in promoting urban development.

Background This study aimed to describe the spread of coronavirus disease 2019 (COVID-19) from Wuhan to rural villages in the Hubei Province. Methods The analysis revealed the following: (1) COVID-19 spread from Wuhan to other cities and rural areas in Hubei, but the disease did not become widespread in rural villages because of strict prevention measures and urbanization; (2) according to the fluctuation in the number of confirmed cases reported in Hubei after January 23, 2020, we inferred that the infected patients on the same day will become ill during the next 20 days; (3) the advanced traffic system facilitates the direct spread of COVID-19 from Wuhan to counties, towns, and villages in the Hubei Province. Results There is a low possibility of COVID-19 spread from prefecture-level cities to rural areas. Conclusions The findings imply that the government should limit the travelers from the cities to the rural areas, pay more attention to the prevention and control of severe acute respiratory syndrome coronavirus 2 in cities, and appropriately prolong the isolated observation period of travelers.

Conceptual Internet of Vehicles (IoV) is viewed as a rising worldview for associated vehicles to trade their data with different vehicles utilizing vehicle-to-vehicle (V2V) interchanges by framing vehicular ad-hoc networks (VANETs), with roadside units utilizing vehicle-to-roadside (V2R) correspondences. Vehicles could quickly find out about the street condition and instantly react and advise drivers for settling on educated choices. Be that as it may, pernicious clients in IoV may deceive the entire interchanges and make mayhem out and about. Information misrepresentation attack is one of the principle security issues in IoV where vehicles depend on data got from different friends/vehicles. In this paper, study about VANET challenges and protocol for security of Information transmission over VANET.

With the acceleration of China’s urbanization process, second and third tier cities have gradually joined the planning and construction of urban rail transit. Second and third tier cities are more suitable for the development of medium-traffic rail transit systems. This paper proposes a novel type of medium-traffic urban traffic system, which combines the advantages of rail transit and public buses. It adopts pure electric drive and applies multi-axle active steering technology to provide a fast and flexible way for public transportation in small and medium-sized cities in China. It’s designed to relieve the problem of travel in cities that are increasingly prominent in China.

Prometno-prostorno planiranje je kontinuirani postupak kojeg je potrebno neprestano unaprjeđivati na temelju analize pokazatelja funkcioniranja sustava. Prema saznanju autora, prilikom planiranja prostora Kampusa Sveučilišta u Rijeci, a ni u vremenu korištenja, nisu detaljno analizirani prometni pokazatelji u svrhu osiguranja više razine pristupačnosti prostora te većeg zadovoljstva korisnika. U radu je prikazana analiza rezultata istraživanja provedenog u ožujku 2020. godine na Građevinskom fakultetu u Rijeci među studentima i zaposlenicima Fakulteta putem online ankete. Cilj istraživanja bio je utvrditi prometne navike ciljane skupine ispitanika vezanih uz njihova dnevna putovanja prema Kampusu. Uz opća obilježja ispitanika i osnovne prometne pokazatelje, kao što su stupanj motorizacije i učestalost korištenja javnog gradskog prijevoza, ispitanici su odgovarali i na specifična pitanja vezana uz korištenje prometne infrastrukture za dolazak na Kampus u prosječnom radnom danu i ocjenjivali kvalitetu postojećeg prometnog sustava s aspekta javnog gradskog prijevoza, infrastrukture za parkiranje te pješačkog i biciklističkog prometa. Na temelju prikupljenih odgovora analizirane su prometne navike studenata i zaposlenika Građevinskog fakulteta u Rijeci te su ispitane korelacije obilježja ispitanika i specifičnih prometnih pokazatelja orijentiranih na putovanja prema Kampusu, a koje mogu pomoći u definiranju budućih planova rekonstrukcije i dogradnje prometne infrastrukture.

The combination of automotive technology and communication networks enables novel systems that improve safety, efficiency, and performance can significantly improve comfort in daily traffic. Vehicle-to-vehicle communication enables new applications through the direct exchange of information between vehicles. In recent decades, this has been intensively researched and standardized technology. The cars thus capture other road users in their environment in smart cities, even beyond visual obstacles. This technology includes digital, wireless communication between vehicles (V2V) or cars and traffic infrastructure (V2I), which is collectively referred to as V2X. V2X communication has a more extended capability range than ultrasonic sensors, cameras, and radars, and can, therefore, alert drivers of dangerous situations earlier and more effectively. Moreover, V2V can be combined with radars and cameras to achieve even greater safety. Vehicle automation and driver assistance systems are also driving forward the promising technology. This paper evaluates state-of-the-art vehicle communication and localization techniques and investigates their applicability on VANET networks for intelligent traffic system.

The relatively new nautical market gave rise to a special kind of tourism. Nautical tourism as a system has the characteristics of a traffic system with all its peculiarities. Nautical tourism is steadily increasing. In countries nurturing this type of economic activity, nautical tourism has the characteristics of mass tourism. Nautical tourism is a complex system consisting of and using various forms of technical and technological processes. As such it is exposed to various risks. Therefore, this paper proposes a systematic approach to the development of safety measures in nautical tourism.

With bus global positioning system data and smart-card data, this study puts forward improved algorithms to further increase the success rate of alighting stop identification for the student group, so that trip chains and origin-destination (OD) matrixes of students can be obtained with the high estimated rate. Aimed at the student group, after using and generalising conventional alighting algorithms, this study innovatively utilises resident and non-resident students' typical trip patterns to further increase the success estimated rate. As a result, the algorithms are verified with a correct estimation of 74.9%, where the success rate steeply increases by 8.6% through the method based on students' typical trip patterns. The empirical analysis and application have shown that the methodology can observe the trip chains of students and optimise the bus service for students. In the future, OD matrixes obtained should be validated with bus OD surveys, and traffic system of metro and shared bikes as well as other datasets such as land use data will be taken into consideration.

Phthalate esters (PAEs) are widely used as plasticizers in consumer products. PAEs are a group of environmental hormone which disrupts human and animals' endocrine systems. Different occupational groups are exposed to various levels of PAEs. In the present study, four typical occupational groups were chosen, including doctors, college teachers, college students, and drivers who worked in public traffic system. In order to understand the exposure levels to PAEs via inhalation, air samples were collected from multiple microenvironments including indoor and outdoor in Hangzhou to measure the gas and particle concentrations of six PAEs, together with time spent in different microenvironments of these four groups. A comprehensive PAEs exposure model was built to estimate the daily PAEs exposure through inhalation, oral and dermal pathways. The Monte Carlo simulation results show that doctors were exposed to the highest level of PAEs, and consequently had the highest health risk among these four occupational groups. In contrast, college students had the lowest health risk. By setting the exposure level of staying in residences as the baseline, doctors and drivers were two occupations exposed to high PAEs health risk. Di-(2-ethylhexyl) phthalate (DEHP) was the largest contributor among the six phthalates, posing moderate health risk (10-5-10-6) to every occupation. For traffic microenvironments alone, the total exposure levels for different transportation modes were in the descending order of busses, cars, cabs, tubes, motor bikes, and walking.

This article describes how the public traffic system evaluation is an important measure to strengthen the management of urban transportation. Many scholars have evaluated the public transportation system, but lack research on different index weights of it. In past models, although the fuzzy assessment method was integrated into an evaluation methodology, its randomness was reflected unclearly. To solve the problems, a fuzzy evaluation of a cloud model is researched. Firstly, the corresponding weights of all indexes are calculated by analytic hierarchy process (AHP) and a clustering method. Then, the principal component of the indexes is extracted by the principal component analysis. According to the distribution of a principal component and processed with the cloud model, a subordinate degree function was established. Finally, scoring cities by combining the principal component weight and membership cloud matrix and evaluating the public transportation system. Comparing the matter-element analysis and the AHP gray model method, this proposed model in this article can evaluate the performance of different urban traffic systems more practically.

For any country’s growth is depend on their people’s growth, whenever we talk about the growth of any country then it shows the achievements done by their peoples. Day by day population are increasing and peoples are enjoying their luxurious life with big bungalows, and big luxurious cars but not a single vehicle it could be per person average of two-three vehicles results to noise, air pollutions and important factor is the increment of traffic of the city. In this paper we are not praising for the growth of the country, no doubt for the growth but we want to focus on the traffic system control. Day to day traffic is increasing in which the big problem is faced for the safe passage for the ambulance vehicle’s escape or some other person who is in need of urgent work anywhere apart from medical facilities. We heard many patients leads to death in the ambulance and doctor says ‘‘it’s too late, if you could reach at least 10minutes before then the patient could have been saved his/her life’’. So here we focused in paper to facilitate the emergency services (only concerned to patient’s ambulance) signal free. This will be possible by using the telecom technologies along with city traffic control stations to implement the things made easier for the signal free passage to escape of ambulance from the traffic lights or streets means the ambulance would not stop in the traffic jams. This can be made possible using VANET-LTE advanced technology.

In this paper, a coupled map car-following model is extended to two-lane traffic system. Based on it, the numerical simulations for one stop and series stops situations are performed respectively under the open boundary condition. The evolution of traffic jam and corresponding energy consumption are then explored and compared each other. Additionally, the influences of the lane changing rule and control scheme on traffic jam and energy consumption are investigated separately. The results show that lane changing rule and control scheme are found not only to suppress the traffic jam but also to reduce the energy consumption. The control scheme exhibit better behavior than lane changing rule does in suppressing traffic jam and reducing energy consumption in two-lane traffic system. The outputs of these findings demonstrate the potential of the proposed model to be applicable to evaluate the control schemes for suppressing traffic jam and reducing energy consumption in real traffic condition.

Variable speed limits (VSL) are used to balance traffic safety and transportation efficiency on the highway. However, driver satisfaction usually affects both aspects because human behaviour is an important factor in the traffic system. Therefore, this study aims to propose a VSL rule that combines traffic flow, risk level and driver satisfaction. First, the authors present a physical model of the lane-changing process, and the psychology of drivers when judging whether to change lanes is measured by mathematical expectation and membership functions. Considering the above three factors as target functions, a new VSL rule for two-lane highways and a periodic VSL rule for three-lane highways are formulated. Simulation results indicate that the rule for two-lane highways has good performance in the three aspects, but when it is extended to the three-lane situation, the driver satisfaction does not improve significantly. However, the periodic speed limit rule can help increase driver satisfaction. Therefore, the VSL rules that are proposed reveal a potentially highly effective way to improve the vehicle speed on the highway and advance rule making.

Roadside dust resulting from industrialization of society has an adverse effect on the environment and human health. However, despite the global research progress in this field, to date, no bibliometric report on the subject has been documented. Hence, bibliometric mapping is important to assess the quality and quantity of the global research activities on road dust. Data were retrieved from the Web of Science Core Collection and Scopus, while RStudio software was used for data analysis. A total of 1186 publications were retrieved from these databases, and progressive growth in the subject over the last 10 years was observed, considering the positive correlation (y = 0.0024 × 3 − 0.1454 × 2 + 2.6061 × −8.5371; R2 = 0.961) obtained for these indices. China had the highest publications, and environmental science-related journals dominated publications on road dust. The findings suggest that other regions of the world, such as the Middle East and Africa, need to channel their research efforts toward this field, considering the shortage of publications on the subject from these regions. Therefore, this study shows that assessing research activity on road dust is important for planning impactful research directions and setting protective and adaptive policies related to the field.

The rail transit has difficulties in meeting daily travel needs of passengers owing to a large population and accelerating urbanization. Analyzing urban travel behaviors with big data helps the design in infrastructures and the optimized personnel allocation. Furthermore, travel behaviors are characterized by dynamic at different time and locations, displaying the rule of urban traffic operation. This paper utilizes smart card data in two cities with different geographical features to analyze the temporal–spatial characteristics of urban travel behaviors. More specifically, by creating travel networks based on the pick-up and drop-off stations and the passenger population among these stations, an interesting observation is that the community structure of travel networks owns a metabolic trend and a stable feature simultaneously. The finding shows that the traffic system can be managed in several parts. Moreover, similar mobility patterns exist in some stations, which can be organized and controlled in the same way. Finally, travel behaviors are related to the urban layout and structure, so the distribution of urban areas can be understood better. Experiments provide enlightening insights for policy makers to comprehend the urban travel behaviors, thus improving the rail transit service plans and scheduling strategies.

With the intensifying energy crisis and environmental pollution, the Energy Internet and corresponding patterns of energy use have been attracting more and more attention. In this paper, the basic concept and characteristics of the Energy Internet are summarized, and its basic structural framework is analyzed in detail. On this basis, couplings between the electric power system and other systems such as the cooling and heating system, the natural gas system, and the traffic system are analyzed, and the operation and planning of integrated energy systems in both deterministic and uncertain environments are comprehensively reviewed. Finally, the research prospects and main technical challenges of the Energy Internet are discussed.

Comprehensive analyses of urban traffic carbon emissions are critical in achieving low-carbon transportation. This paper started from the architecture design of a carbon emission mobile monitoring system using multiple sets of equipment and collected the corresponding data about traffic flow, meteorological conditions, vehicular carbon emissions and driving characteristics on typical roads in Shanghai and Wuxi, Jiangsu province. Based on these data, the emission model MOVES was calibrated and used with various sensitivity and correlation evaluation indices to analyze the traffic carbon emissions at microscopic, mesoscopic and macroscopic levels, respectively. The major factors that influence urban traffic carbon emissions were investigated, so that emission factors of CO, CO2 and HC were calculated by taking representative passenger cars as a case study. As a result, the urban traffic carbon emissions were assessed quantitatively, and the total amounts of CO, CO2 and HC emission from passenger cars in Shanghai were estimated as 76.95kt, 8271.91kt, and 2.13kt, respectively. Arterial roads were found as the primary line source, accounting for 50.49% carbon emissions. In additional to the overall major factors identified, the mobile monitoring system and carbon emission quantification method proposed in this study are of rather guiding significance for the further urban low-carbon transportation development.