A Systematic Cooperation Method for In-Car Navigation Based on Future Time Windows

Traffic congestion has become a severe problem, af-fecting travellers both mentally and economically. To al-leviate traffic congestion, this paper proposes a method using a concept of future time windows to estimate the future state of the road network for navigation. Through our method, we can estimate the travel time not only based on the current traffic state, but the state that ve-hicles will arrive in the future. To test our method, we conduct experiments based on Simulation of Urban MO-bility (SUMO). The experimental results show that the proposed method can significantly reduce the overall travel time of all vehicles, compared to the benchmark Dijkstra algorithm. We also compared our method to the Dynamic User Equilibrium (DUE) provided by SUMO. The experimental results show that the performance of our method is a little better than the DUE. In practice, the proposed method takes less time for computation and is insensitive to low driver compliance: with as low as 40% compliance rate, our method can significantly im-prove the efficiency of the unsignalised road network. We also verify the effectiveness of our method in a signalised road network. It also demonstrates that our method can assign traffic efficiently

A Method for Traffic Flow Forecasting in a Large-Scale Road Network Using Multifeatures

Accurate traffic prediction on a large-scale road network is significant for traffic operations and management. In this study, we propose an equation for achieving a comprehensive and accurate prediction that effectively combines traffic data and non-traffic data. Based on that, we developed a novel prediction model, called the adaptive deep neural network (ADNN). In the ADNN, we use two long short-term memory (LSTM) networks to extract spatial-temporal characteristics and temporal characteristics, respectively. A backpropagation neural network (BPNN) is also employed to represent situations from contextual factors such as station index, forecast horizon, and weather. The experimental results show that the prediction of ADNN for different stations and different forecast horizons has high accuracy; even for one hour ahead, its performance is also satisfactory. The comparison of ADNN and several benchmark prediction models also indicates the robustness of the ADNN

Modelling and Simulation of Cooperative Control for Bus Rapid Transit Vehicle Platoon in a Connected Vehicle Environment

The aim of this paper is to develop a cooperative control model for improving the operational efficiency of Bus Rapid Transit (BRT) vehicles. The model takes advantage of the emerging connected vehicle technology. A connected vehicle centre is established to assign a specific reservation time interval and transmit the corresponding dynamic speed guidance to each BRT vehicle. Furthermore, a set of constraints have been set up to avoid bus queuing and waiting phenomena in downstream BRT stations. Therefore, many BRT vehicles are strategically guided to form a platoon, which can pass through an intersection with no impedance. An actual signalized intersection along the Guangzhou BRT corridor is employed to verify and assess the cooperative control model in various traffic conditions. The simulation-based evaluation results demonstrate that the proposed approach can reduce delays, decrease the number of stops, and improve the sustainability of the BRT vehicles.</p

Data-driven spatial-temporal analysis of highway traffic volume considering weather and festival impacts

This paper aims to discover the relationships among the weather, holidays, and the traffic volume using multisource data from the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and to reveal the corresponding regional spatial–temporal traffic and migration patterns. Using accurate hourly weather and traffic volume data, this study examines the traffic volume from the origin to the destination county by considering traffic factors, weather factors, and temporal factors. A Random-effect regression model and a random forest model are established to analyze the above factors and identify the factors that contribute to the annual variation in traffic patterns. An RER + RF fusion prediction model based on ridge regression is proposed to predict the hourly traffic volume from origin to destination county, and is adopted in the spatial–temporal submodels. The results show that the impact of rainfall on traffic volume varies as the rainfall varies, and a rain-induced traffic pattern shift towards highway travel is found, which interacts with the negative effect of rainfall on highway traffic volumes. The Spring Festival holiday witnesses a V-shaped traffic volume curve during the study period. Some traffic pattern differences are also found in different spatial–temporal submodels. The RER + RF fusion model performs better in predicting in parent model and most of the spatial–temporal submodels, which validates the proposed model in predicting the traffic volume. The findings can provide transport agencies, urban planning agencies, and urban agglomeration travelers with valuable information for highway transport activity analysis considering the effects of weather and festival events

Research on the Further Role of Retired Cadres in Agricultural Research Units Under the New Situation—Case Study of Chinese Academy of Tropical Agriculture Sciences

This paper elaborated the importance of retired researchers play a great role in the agricultural research units under the new era, and it takes Chinese Academy of Tropical Agriculture Sciences (Hereinafter referred to as CATAS) as a example to analyze the current situation of the role and the advantages of retired researchers. Thereby, it suggests the approach of the role played by retired researchers under the new era and the effective measures for the further role of retired researchers from strengthening ideological and political construction, improving the living conditions and the level of spiritual and cultural, active aging, strengthening service management and other aspects.Key words: Retired cadres; Agricultural research units; Rol

Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM

Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station

Bus timetable optimization model in response to the diverse and uncertain requirements of passengers for travel comfort

Most existing public transit systems have a fixed dispatching and service mode, which cannot effectively allocate resources from the perspective of the interests of all participants, resulting in resource waste and dissatisfaction. Low passenger satisfaction leads to a considerable loss of bus passengers and further reduces the income of bus operators. This study develops an optimization model for bus schedules that considers vehicle types and offers two service levels based on heterogeneous passenger demands. In this process, passenger satisfaction, bus company income, and government subsidies are considered. A bilevel model is proposed with a lower-level passenger ride simulation model and an upper-level multiobjective optimization model to maximize the interests of bus companies, passengers, and the government. To verify the effectiveness of the proposed methodology, a real-world case from Guangzhou is presented and analyzed using the nondominated sorting genetic algorithm-II (NSGA-II), and the related Pareto front is obtained. The results show that the proposed bus operation system can effectively increase the benefits for bus companies, passengers, and the governmen

Flexible transit routing model considering passengers’ willingness to pay

This paper proposes an alternative flexible transit model with two levels of bus stops, A level and B level. A-level bus stops are fixed, while B-level bus stops are flexible and provide service only when passengers indicate a strong willingness to pay (WTP). This fare structure encourages passengers to choose bus stops with their mobile phones or computers. An optimization model of 0-1 integer-programming is formulated based on whether certain B-level stops can be serviced. With a numerical example, we compare the performance of the proposed traversing method and a tabu search algorithm, both of which are adapted to solve the model. Finally, a real case is provided to evaluate the proposed transit system against comparable systems (e.g., a fixed-route transit system and a taxi service), and the result shows that the flexible transit routing model will help both passengers and bus companies, thus creating a win-win situation

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes