Smart Transportation Systems 2020

This paper presents a modeling approach developed within the MOTUS project, designed to provide a standardized and solid intervention proposal to face events and disruption on a public transport network. This modeling approach resulted into a tool capable of identify in a formalized way the nodes and links where to broadcast info-mobility information through ITS systems and to lead the users to the best alternative solutions. The tool is exploited to make the decision process less dependent on the expert judgment (that still plays a vital role) and human factors, to allow the service provider to respond in a faster and clear way to the possible disruptions both through info-mobility and the strengthening of the offer on the involved routes. Therefore, this paper describes how the modeling approach is applied, how the resulting tool can be exploited, and finally provides an example on the city of Milan, simulating the closure of one of the main lines and reporting the results provided by the presented model and the developed tool.

Bus fleet electrification achieves momentum and inspiration within public transport aiming at further improving the mobility sustainability. In many countries, such as Sweden, China, and the USA, there are several ongoing demonstration projects of electric buses and many research projects. The charging technology development and implication is key for the expansion of electric buses and to foster it. An investigation of characteristics and benefits of various existing and future charging technologies has been created in this paper. The main types of charging infrastructure are depot charging, station charging, and inductive charging. The choice of different types is highly related to infrastructure construction, investment, and daily operation. The detailed illustration and analysis of them can provide a solid foundation to the near-future large-scale electric buses’ operation.

The promotion of electric buses is of great significance for reducing vehicle emission, decreasing operation costs of transit corporations and workloads of bus drivers. However, the adoption of electric buses is constrained by their limited driving range. To guarantee the regular level of service, electric buses need to get recharged during daily operating hours. Electric bus battery life is highly correlated to charging modes. In this study, we proposed a mixed charging strategy with the setup of lower and upper limits of battery state of charge (SOC). A bi-level optimization model for electric bus scheduling was developed considering bus fleet size, variance of travel times of all buses and their idling times. The lower-level model is to minimize the variance of travel times and to maximize the average idling times of all buses. The upper-level model is to minimize the extra economic cost resulting from the bus fleet expansion. A case study is conducted to assess the proposed optimization model with a real electric bus route. The results show that the proposed model is capable of maintaining bus battery SOC within the reasonable range.

Accurate modeling of travel choice behavior is crucial for effective transport demand forecasting, management and planning. This study tries to shed light on the appropriate modeling approach concerning the influences of in-vehicle crowding on mode choice behavior in the multimodal network. Stated preference surveys covering four commuting transport modes and four influencing factors are conducted to collect empirical behavior data. Three modeling methods, treating the in-vehicle crowding as a moderator of perceived travel time, as an independent variable and by incorporating interaction effect, are empirically compared. The result indicates that there is a bidirectional interaction between travel time and in-vehicle crowding. The influence of in-vehicle crowding increases with increasing travel time and vice versa. Considering crowding as an independent variable and taking the effects of travel time on the perception of in-vehicle crowding are the best ways to depict the overall influences of in-vehicle crowding. The sensitivity analysis shows that increasing the cost of using car is comparatively effective for reducing car usage. Shortening the travel time of public transit and improving service quality such as travel time reliability and in-vehicle crowding are more useful in attracting car users as compared to reduction in the cost of public transit. The results provide insights into travelers’ behavior in the multimodal network and could support scientific transport management and planning.

Traffic speed has been traditionally used as a measure of traffic performance. Predicting the traffic speed is fundamental for efficient traffic management and control strategy. This study explores the influences of freeway attributes, weather, and air condition on traffic speed. A quantitative model is also introduced to predict the traffic speed as per the identified influencing factors. Empirical data of traffic flow and potential influencing factors are collected from multiple sources for analysis and model calibration. The principal component analysis is firstly conducted to select the significant variables influencing the traffic speed. Afterward, a multiple linear regression model is calibrated to quantitatively model the impacts of different factors and investigate their weights. The results show that the attributes of freeway, the humidity of the area, the temperature, the horizontal visibility, the station maker, the air quality, and the PM quality have influences on the traffic speed. Among all of the variables, the weight of the existence of toll station is highest, indicating the largest influence on the traffic speed.

The bike-sharing service system is a service that allows a customer to rent a bike from a bike-sharing station and then return it to another bike-sharing station in a short time after they reach their destination. Thus, the impact of the bike distribution system based on the frequency of bike usage needs to be assessed. The bike-sharing system operator needs to predict the demand to accurately know how many bikes are needed in every station so as to assist the planner in the management process of the bike-sharing stations. This paper proposes an efficient and accurate model for predicting the bike-sharing service usage using various features of a machine learning algorithm. We compared the exiting techniques for the sequential data predicting of artificial intelligence for time series data and analysis. Then, we considered the use of the multivariate model with a recurrent neural network (RNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU). In addition, we considered combining the LSTM and GRU methods together to improve the model’s effectiveness and accuracy. The results showed that all the RNNs, including the LSTM, GRU, and the model combining the LSTM and GRU, are able to achieve high performance using the mean square mean absolute, mean squared error, and root mean square error. However, the mixed LSTM–GRU model accurately predicted the demand in this case.

With the continuous growth of the number of Internet users and the continuous expansion of the online retail market in China, consumers have put forward higher requirements for logistics services. Thus, this study uses a binary logit model to study China’s logistics services from the perspective of customers, and the main factors affecting customer satisfaction with logistics services were explored. First, questionnaire was scientifically designed, and a total of 356 samples was collected in the online survey, of which 310 samples considered to be valid, and the questionnaire effective rate was 87.1%. Second, based on the survey data, the reliability and validity of the questionnaire were tested and factor analysis was performed. The results showed that the reliability and validity of the questionnaire were good, and the factor structure of the questionnaire could meet the needs of this study. Finally, the binary logit model was used to analyze the main factors affecting customer satisfaction with logistics services. The results show that facilities, convenience, reliability, empathy, economics and timeliness have a significant impact on logistics service satisfaction, and among them, facilities, economics and convenience are the most important factors. The research results can effectively improve China’s overall logistics service level and have strong practical significance.

Traffic flow fundamental diagram, or simply speed–density relationship and/or flow–density relationship, is the basis of traffic flow theories and road performance studies since it depicts the mathematical relationship among three traffic flow fundamental parameters—density, speed, and traffic flow. In this paper, through mathematical analyses and simulations, we find that for all existing fundamental diagram models, their derived microscopic car following models do not perform well and cannot reproduce the status of the stable flow described by the corresponding fundamental diagrams. The results indicate that there seems to exist a hidden gap between existing traffic flow fundamental diagrams and the corresponding microscopic car following models. We further discuss about the fundamental causes behind such gap and propose a simple yet incomplete solution at the end of this paper.

In this paper, we propose a leader-based control strategy for vehicle platoon at signalized intersections. The speed guidance which regards the states of followers and the signal phase and timing (SPaT) information is adopted for the leader of the platoon. The Cooperative Adaptive Cruise Control (CACC) with a piecewise policy is used to control the followers. The efficiency of the signalized intersection is specifically considered when constructing the control strategy. The simulation results show that the proposed strategy can control the vehicle platoon smoothly cross signalized intersections without stop and ensure the traffic efficiency simultaneously.

With the progress of urbanization, car ownership is experiencing explosive growth in China, which leads to heavy pressure on the urban road network. Arterial coordination strategy has been proved an effective method to avoid or alleviate traffic congestion. However, with the increasing proportion of flow on the minor arterial, arterial coordination efficiency might be affected. To figure out the problem, a numerical test is conducted by designing eight scenarios with different proportion of through movement and left turn flow on the minor arterials. MAXBAND model is applied for optimizing signal plans. The results show that average delay for vehicles on the arterials increases with the increasing of proportion of through movement flow, as well as the entire average delay. Average delay for vehicles on the minor arterials and two-way bandwidth decreases at same time. In other words, when the proportion of minor arterial flow increases, the arterial coordination efficiency would be reduced, especially for increasing left turn flow. This work reveals the improvement direction for arterial coordination.

Sections of tunnel entrances, industrial and mining schools with deceleration function zones are high-traffic zones due to their special traffic conditions. The instability of the car during the deceleration process and the driver’s wrong deceleration operation may be important causes of traffic accidents. In order to improve the driving safety in the road deceleration function zone, the traffic flow at the entrance to Tianhe North Tunnel in Guangzhou City is taken as the research object, and we evaluate the traffic safety in the road deceleration function zone. The results show that speed standard deviation is a good predictor of potential risks, and speed standard deviation can be used to actively assess road safety. The research results help to further to optimize the driving behavior in the deceleration functional area and improve the safety of traffic flow in the deceleration functional area.

Industrial vehicle automation is a core component of the building Industry 4.0. The uses of self-driving vehicles, inspection robots, and vehicular ad hoc networks (VANETs) communications in the mining industry are expected to open significant opportunities for collecting and exchanging data, localization, collision warning, and up-to-date traffic to enhance both the safety of workers and increase the productivity. In this paper, we present a review of the large-scale fading channel at 5.9 GHz in confined areas. Then, the requirements for DSRC receiver performance for VANET applications in an underground mine is calculated. This paper also reports the overall performance evaluation of three existing routing protocols, namely, emergency message dissemination for vehicular environments (EMDV), enhanced multi-hop vehicular broadcast (MHVB), and efficient directional broadcast (EDB) for active safety applications. Finally, a comparative study of these three routing protocols for cooperative collision warning in underground mining galleries was evaluated.

This work aims to evaluate the real-time processing system in the context of an autonomous vehicle with limited hardware and software capabilities. We elaborate algorithm implemented in 1/10 scale electric car using a line scan camera, speed sensors, and embedded electronic control system. The vehicle navigates in an arbitrary one-lane circuit using an edge detection algorithm. The challenge was to make a complete one loop of the arbitrary circuit in the shortest time with various lighting conditions. The experiments show that several issues were revealed in each step of data sensors processing and need a robust algorithm to handle exceptions caused by multiple disturbances. Furthermore, we paid particular attention to time constraints in embedded processor calculation and actuators response time to achieve reliable critical software control algorithms.

Limited efforts have been made to unveil the factors affecting the severity of secondary incidents. Compared to primary incidents, secondary incidents are more injury and fatality prone. Secondary incidents that occurred on the Interstate-5 in California within five years were collected. Detailed real-time traffic flow data, geometric characteristics and weather conditions were obtained. First, a random forest-based (RF) feature selection approach was adopted. Then, support vector machine (SVM) models were developed to investigate the effects of contributing factors. For comparison, RF and ordered logistic (OL) models were also built based on the same dataset. It was found that the SVM model has high capacity for solving classification problems with limited data availability. Further, sensitivity analysis assessed the impacts of explanatory variables on the injury severity level. The results can provide guidance for the development of countermeasures and improvement of road safety policies to potentially reduce road trauma caused by secondary incidents.

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 headways between vehicles in the traffic flow of intersections are one of the crucial variables for reasonable signal timing setting and intersection configuration design. Many studies apply constant discharge headways to calculate the saturation flow rate, and scarce studies quantitatively investigate the relationship of headway and speed in the saturation flow. This study endeavours to model the headway–speed relationships of saturation traffic flow at the signalised intersection. Five typical intersections with large traffic demand in Golden Coast City are surveyed to collect data regarding vehicles’ discharging speed and headways. The least squared method and the fitting degree test are applied to model the headway–speed relationships at the signalised intersections and compare the models’ fitting performance. The results indicate that the headway is significantly associated with speed. The headway increases with decreasing speed crossing the intersections. The empirically and quantitatively calibrated relationships between speed and headway can be used to calculate the saturation flow rate in the intersections with different discharging speeds and further support the design of intersections with large traffic demand.

Air pollution and climate change arouse consistent attention of international community. Shipping industry, being one of the most important transport methods, carries more than 80% of the total international trade and has been recognized as a potential source of air pollutant mitigation. In order to reduce emissions of marine traffic, especially in the area of coastal waters, regulations about the quality of marine fuel have been carried out, and the maximum sulphur content allowed for marine fuel becomes increasingly stringent as time goes by. In order to comply with the regulations, shipping has to take various measures, including adopting electric power from shore while berthing. Shore-side electricity, also called cold ironing, refers to the use of electricity from shore side while berthing at the port instead of auxiliary engine. In recent years, shore power has been adopted in an increasing number of ports; in China, most ports are able to provide shore power for ships while berthing. For ships with shore power facilities, the price of shore-side electricity is an element that can influence their choice of port to visit. It is an incentive for ports to lower the power price. This paper tends to investigate what is the best price to maximize the port’s total benefit in the competition with other ports in the same group. In order to describe the competition among ports, game theory is applied, and the Bertrand model is adopted.

Air pollution is an issue that has been widespread concern in all sectors of society. The pollutants, including toxic gases, greenhouse gases, and particulate matters, have permeated every aspect of our daily life and have a negative impact on human health, agriculture, industry, and climate change. Found by hard and thorough search, the human activities account for the majority and the transport sector is one of the most challenging areas, when it comes to abatement of local air pollution. Marine traffic, which covers over 80% of international trade, is mainly powered by cheap fuel oil with high impurities, so it will affect the social welfare of the coastal areas. Various measures that can be adopted to alleviate the problem to customize suitable regulations through research of the emission from shipping should be conducted. Also, the emission evaluation is critical to measure the efficiency of the regulation. Therefore, following the main steps of Ship Traffic Emissions Assessment Model, we summarize an activity-based framework of shipping emission evaluation that takes advantage of data from automatic identification system.