Dynamic Demand Forecast and Assignment Model for Bike-and-Ride System

Bike-and-Ride (B&R) has long been considered as an effective way to deal with urbanization-related issues such as traffic congestion, emissions, equality, etc. Although there are some studies focused on the B&R demand forecast, the influencing factors from previous studies have been excluded from those forecasting methods. To fill this gap, this paper proposes a new B&R demand forecast model considering the influencing factors as dynamic rather than fixed ones to reach higher forecasting accuracy. This model is tested in a theoretical network to validate the feasibility and effectiveness and the results show that the generalised cost does have an effect on the demand for the B&R system.</p

Adaptive vehicle extraction in real-time traffic video monitoring based on the fusion of multi-objective particle swarm optimization algorithm

Abstract In view of the problems in the real-time traffic video monitoring that the adaptive vehicle extraction is greatly affected by the environmental factors such as the illumination, noise, and so on; the missed detection and error detection rate is high; and it is difficult to meet the robustness and the real-time performance at the same time, a kind of method for the adaptive vehicle extraction in real-time traffic video monitoring based on the fusion of multi-objective particle swarm optimization algorithm is put forward. In this method, based on the multi-objective particle swarm optimization algorithm, adaptive binarization processing is carried out on the image first, and the interference points are removed by filtration through the erosion and expansion method. Simple and effective method is used to carry out the merger of the shadow line and the extraction of the real-time traffic video. In the algorithm, the information entropy in the target area and the symmetry characteristics of the vehicle tail are used to screen and identify the region of interest, which has reduced the missed detection and error detection rate of the algorithm. The multi-objective particle swarm optimization algorithm is used to extract the vehicle boundaries and has achieved relatively good effect. The results show that the detection accuracy is 89% and the average operating speed is 17.6 frames/s during the processing of the real-time traffic video with the resolution of 640 × 480

A Model to Research Off-street Parking Across the Travel Time Based on the Impact of Non-motorized Traffic and Other Factors

AbstractIn this paper, we takes the parking vehicle out of the off-street parking access in the city as the research object, analyzes the main influence factors of the vehicle in the process of entering in the parking lot, and the crossing time is defined as the cross through the waiting time and travel time. By the way of theoretical analysis and field investigation, we present one calculation model through the waiting time while another through the travel time, and verify the analysis. Based on the measured data about parking entering rate, we conducted a sensitivity analysis the combination conditions of different non-motorized vehicle lane width and flow, the main conclusions are as follow: First of all, compared with the non-motorized vehicle lane width, the impact of non-motorized vehicle flow on crossing time is more obvious; Secondly, in the same flow conditions, changes of crossing time and non-motorized vehicle lane width is inversely proportional to the width of non-motorized vehicle lane; Thirdly, when less than 5 meters, crossing time changed significantly affected by it; finally, when the non-motorized vehicle lane width is greater than 6 meters, crossing time had small changes affected by it

Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data

To prevent and control public transport safety accidents in advance and guide the safety management and decision-making optimization of public transport vehicles, based on the forewarning and other multisource data of public transport vehicles in Zhenjiang, holographic portraits of public transport safety operation characteristics are constructed from the perspectives of time, space, and driver factors, and a prediction model of fatigue driving and driving risk of bus drivers based on BP neural network is constructed. Finally, model checking and virtual simulation experiments are carried out. The result of the research shows that the driver’s fatigue risk during the period of 7 : 00-8 : 00 am is much higher than other periods. When the bus speed is about 30 km/h, the driver fatigue forewarning events occur the most. Drivers aged 30–34 years have the largest proportion of vehicle abnormal forewarning, drivers aged 40–44 years have the largest proportion of fatigue forewarning events, and drivers with a driving experience of 15–19 years have the largest overall proportion of various forewarning events. When the vehicle speed range is (18, 20) km/h and (42, 45) km/h, the probability of fatigue driving risk and driving risk forewarning increases sharply; and when the vehicle speed is lower than 17 km/h or 41 km/h, the probability of fatigue driving risk and driving risk forewarning, respectively, is almost zero. The probability of fatigue forewarning during low peak hours on rainy days is about 30% lower than that during peak hours. The probability of driving forewarning during flat peak hours is 15% higher than that during low peak hours and about 10% lower than that during peak hours. This paper realized for the first time the use of real forewarning data of buses in the full time, the whole region, and full cycle to carry out research. Related results have important theoretical value and practical significance for scientifically guiding the safety operation and emergency management strategies of buses, improving the service level of bus passenger transportation capacity and safety operation, and promoting the safety, health, and sustainable development of the public transportation industry

Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data

To prevent and control public transport safety accidents in advance and guide the safety management and decision-making optimization of public transport vehicles, based on the forewarning and other multisource data of public transport vehicles in Zhenjiang, holographic portraits of public transport safety operation characteristics are constructed from the perspectives of time, space, and driver factors, and a prediction model of fatigue driving and driving risk of bus drivers based on BP neural network is constructed. Finally, model checking and virtual simulation experiments are carried out. The result of the research shows that the driver’s fatigue risk during the period of 7 : 00-8 : 00 am is much higher than other periods. When the bus speed is about 30 km/h, the driver fatigue forewarning events occur the most. Drivers aged 30–34 years have the largest proportion of vehicle abnormal forewarning, drivers aged 40–44 years have the largest proportion of fatigue forewarning events, and drivers with a driving experience of 15–19 years have the largest overall proportion of various forewarning events. When the vehicle speed range is (18, 20) km/h and (42, 45) km/h, the probability of fatigue driving risk and driving risk forewarning increases sharply; and when the vehicle speed is lower than 17 km/h or 41 km/h, the probability of fatigue driving risk and driving risk forewarning, respectively, is almost zero. The probability of fatigue forewarning during low peak hours on rainy days is about 30% lower than that during peak hours. The probability of driving forewarning during flat peak hours is 15% higher than that during low peak hours and about 10% lower than that during peak hours. This paper realized for the first time the use of real forewarning data of buses in the full time, the whole region, and full cycle to carry out research. Related results have important theoretical value and practical significance for scientifically guiding the safety operation and emergency management strategies of buses, improving the service level of bus passenger transportation capacity and safety operation, and promoting the safety, health, and sustainable development of the public transportation industry.</jats:p

Dynamic flow analysis and crowd management for transfer stations: a case study of Suzhou Metro

Transfer stations are important nodes in the metro network, and it is of great significance to study the coordinated organization scheme between passenger demand and facility configuration, in order to improve the transportation efficiency. Taking the Dongfangzhimen station of the Suzhou Metro as the research object, this paper starts by analyzing the configuration of service-oriented facilities, and then dissects the spatiotemporal distribution characteristics and individual behavior characteristics of passengers via data processing, where a meticulous analysis has been carried out based on the automatic fare collection data and the field investigation data. On this basis, we have discussed the performance and bottlenecks under the current facility configuration and flow organization scheme, constructed the simulation scene of a representative peak hour in holidays using AnyLogic, and put forward a volume-based path organization scheme considering the coordination with flow demand, as well as a plan of multi-stage crowd management. As evidenced by the results, the improved scheme exhibits a higher adaptability to passenger flow and a greater balance among facility utilization, where the maximum queue length, the average time consumption and the average standing density have been reduced by 31%, 14% and 6%, respectively. The proposed methods of data analysis, flow organization and strategy decision are reliable and applicable to the management of metro transfer stations

A study of high-speed train delays and relevant propagation influence characteristics.

Faced with the complex and diversified disturbances of abnormal events in high-speed railway, studying the characteristics of delay scenario parameters and related propagation influence laws plays a fundamental role in analyzing the applicability of operation plans, formulating train operation adjustments, and evaluating the operation decision plans, as well as providing practical data support for the establishment of theoretical models. Based on the mechanical analysis of the primary and knock-on delays, this paper combines the daily safety information data and timetable data, and takes into account the modified primary delays upon speed loss, by taking the high-speed railway network in the Yangtze River Delta region as an example. On this basis, this paper further studies the statistical distribution of primary delay duration, event location distribution, emergent measures distribution, occurrence time and event cause distribution, and other characteristics. Based on this research, the distribution characteristics of the influenced train number and the cumulative delay under kinds of disturbances have been discussed, where the effects of running redundancy, propagation rate, and related parameters on delay propagation have got quantitative analysis. Research shows that the number of abnormal event samples distributed in the sections is 1.53 times that of stations. Based on the Fuzzy C-Means clustering results, abnormal events in the section are more likely to propagate than abnormal events at stations. For trains experiencing primary delay, the relationship between the maximum cumulative train delay and primary delay mostly obey a power-law distribution and the values of R2 are all greater than 0.65, indicating a high correlation at the theoretical level

Collaborative multicenter logistics delivery network optimization with resource sharing

Collaboration among logistics facilities in a multicenter logistics delivery network can significantly improve the utilization of logistics resources through resource sharing including logistics facilities, vehicles, and customer services. This study proposes and tests different resource sharing schemes to solve the optimization problem of a collaborative multicenter logistics delivery network based on resource sharing (CMCLDN-RS). The CMCLDN-RS problem aims to establish a collaborative mechanism of allocating logistics resources in a manner that improves the operational efficiency of a logistics network. A bi-objective optimization model is proposed with consideration of various resource sharing schemes in multiple service periods to minimize the total cost and number of vehicles. An adaptive grid particle swarm optimization (AGPSO) algorithm based on customer clustering is devised to solve the CMCLDN-RS problem and find Pareto optimal solutions. An effective elite iteration and selective endowment mechanism is designed for the algorithm to combine global and local search to improve search capabilities. The solution of CMCLDN-RS guarantees that cost savings are fairly allocated to the collaborative participants through a suitable profit allocation model. Compared with the computation performance of the existing nondominated sorting genetic algorithm-II and multi-objective evolutionary algorithm, AGPSO is more computationally efficient. An empirical case study in Chengdu, China suggests that the proposed collaborative mechanism with resource sharing can effectively reduce total operational costs and number of vehicles, thereby enhancing the operational efficiency of the logistics network.</jats:p