Travel time reliability is of increasing importance for travelers, shippers, and transportation managers because traffic congestion has become worse in major urban areas in recent years. To better evaluate the urban network-wide travel time reliability, five indices based on the emerging on-demand ride service data are proposed: network free flow time rate (NFFTR), network travel time rate (NTTR), network planning time rate (NPTR), network buffer time rate (NBTR), and network buffer time rate index (NBTRI). These indices take into account the probability distribution of the travel time rate (i.e., travel time spent for the unit distance, in min/km) of each origin-destination (OD) pair in the road network. We use real-world data extracted from DiDi-Chuxing, which is the largest on-demand ride service platform in China. For demonstrative purposes, the network-wide travel time reliability of Beijing is analyzed in detail from two dimensions of time and space. The results show that the road network is more unreliable in AM/PM peaks than other time periods, and the most reliable time period is the early morning. Additionally, we can find that the central region is more unreliable than other regions of the city based on the spatial analysis results. The proposed network travel time reliability indices provide insights for the comprehensive evaluation of the road network traffic dynamics and day-to-day travel time variations.
This paper proposes an improved multi-class dynamic network traffic flow propagation model with a consideration of physical queues. Each link is divided into two areas: Free flow area and queue area. The vehicles of the same class are assumed to satisfy the first-in-first-out (FIFO) principle on the whole link, and the vehicles of the different classes also follow FIFO in the queue area but not in the free flow area. To characterize this phenomenon by numerical methods, the improved model is directly formulated in discrete time space. Numerical examples are developed to illustrate the unrealistic flows of the existing model and the performance of the improved model. This analysis can more realistically capture the traffic flow propagation, such as interactions between multi-class traffic flows, and the dynamic traffic interactions across multiple links.
Train speed trajectory optimization is a significant issue in railway traffic systems, and it plays a key role in determining energy consumption and travel time of trains. Due to the complexity of real-world operational environments, a variety of factors can lead to the uncertainty in energy-consumption. To appropriately characterize the uncertainties and generate a robust speed trajectory, this study specifically proposes distance-speed networks over the inter-station and treats the uncertainty with respect to energy consumption as discrete sample-based random variables with correlation. The problem of interest is formulated as a stochastic constrained shortest path problem with travel time threshold constraints in which the expected total energy consumption is treated as the evaluation index. To generate an approximate optimal solution, a Lagrangian relaxation algorithm combined with dynamic programming algorithm is proposed to solve the optimal solutions. Numerical examples are implemented and analyzed to demonstrate the performance of proposed approaches.
In large cities with heavily congested metro lines, unexpected disturbances often occur, which may cause severe delay of multiple trains, blockage of partial lines, and reduction of passenger service. Metro dispatchers have taken a practical strategy of rescheduling the timetable and adding several backup trains in storage tracks to alleviate waiting passengers from crowding the platforms and recover from such disruptions. In this study, we first develop a mixed integer programming model to determine the optimal train rescheduling plan with considerations of in-service and backup trains. The aim of train rescheduling is to frequently dispatch trains to evacuate delayed passengers after the disruption. Given the nonlinearity of the model, several linearization techniques are adapted to reformulate the model into an equivalent linear model that can be easily handled by the optimization software. Numerical experiments are implemented to verify the effectiveness of the proposed train rescheduling approach.
In the EU Horizon 2020 Shift2Rail Multi-Annual Action Plan, the challenge of railway maintenance is generating knowledge from data and/or information. Therefore, we promote a novel concept called “IN2CLOUD,” which comprises three sub-concepts, to address this challenge: 1) A hybrid cloud, 2) an intelligent cloud with hybrid cloud learning, and 3) collaborative management using asset-related data acquired from the intelligent hybrid cloud. The concept is developed under the assumption that organizations want/need to learn from each other (including domain knowledge and experience) but do not want to share their raw data or information. IN2CLOUD will help the movement of railway industry systems from “local” to “global” optimization in a collaborative way. The development of cutting-edge intelligent hybrid cloud-based solutions, including information technology (IT) solutions and related methodologies, will enhance business security, economic sustainability, and decision support in the field of intelligent asset management of railway assets.
The involvement of the private sector in the construction or operation of an infrastructure project may enhance the financial viability of projects, which facilitates the formation of public-private partnership (PPP) for project delivery. PPP exploits the strength of the private sector by shifting certain project risks from the public party to the private sector who can efficiently manage certain risks. In joint railway and housing development, the approach of bundling railway and housing development (R&HD) allows cross-subsidization between immense railway construction cost and profitable housing rental revenue. This approach also provides flexibility in incorporating PPP models by distributing railway and housing revenues and costs and their inherent risks properly to the public and private sectors. Ng and Lo (2015a) developed an evaluation framework for joint railway and property development, which evaluates PPPs based on financial and construction criteria for selecting the best suitable PPP for a particular project. This study, which is based on the framework in Ng and Lo (2015a), aims to examine the robustness of various PPP configurations. This study analyzes the effects of PPP configurations on stakeholders’ risks and returns under population or demand growth and railway construction cost uncertainties. The eventual outcome of particular PPP configurations is also examined. This study also seeks to answer the following questions: How would optimal configuration change under highly volatile population and railway construction cost? Are there PPP configurations that are robust to these uncertainties and those that are sensitive to a particular uncertainty? This understanding is critical for managing risks and facilitating the formation of appropriate PPP for R&HD.
The overlap of bus and rail transit routes is common in China. This overlap provides passengers multiple choices for one trip. However, the availability of multiple options would cause uncertainty in the travel distribution of passengers. Given that buses and rail transits are becoming increasingly common, this paper aims to present the overlapped operation condition of bus and rail transit using a bi-level model from the perspective of bus operators. Frequency optimization model is established in the upper-level model. A heuristic algorithm called shuffled complex evolution (SCE-UA) method is used to solve the established frequency optimization model, and three other heuristic methods are compared with SCE-UA. A lower-level Logit model based on Agent simulation is set for traffic mode split. Data on the transit system in Dalian city are chosen as an example to test the feasibility of the model and the algorithm. Results show that as the overlapped optimization of bus route and rail transit routes changed primary bus frequency, the use of SCE-UA to solve such problems has evident advantages and feasibility; furthermore, changed bus frequency would improve bus operations.
This paper considers multiple perspectives to explore the concept of high-speed railway (HSR), rationally abstract its value formation mechanism, and quantitatively measure its actual performance. This paper analyzes the governance potential of major countries in the high-speed railway value chain and studies the feasible ways and development strategies to enhance the high-speed railway governance in China. Findings of this paper are as follows. First, the government, as the early manager of high-speed railway governance, has given way to Siemens and other integrated enterprises. Second, the high-speed railway standard output has become the core competitiveness that embodies high-speed railway. Third, the global high-speed railway market presents a hierarchical high-speed railway governance model and changes to a modular approach to governance.
Bi-directional pedestrian flows are common at crosswalks, footpaths, and shopping areas. However, the properties of pedestrian movement may vary in urban areas according to the type of walking facility. In recent years, crowd movements at carnival events have attracted the attention of researchers. In contrast to pedestrian behavior in other walking facilities, pedestrians whose attention is attracted by carnival displays or activities may slow down and even stop walking. The Lunar New Year Market is a traditional carnival event in Hong Kong held annually one week before the Lunar New Year. During the said event, crowd movements can be easily identified, particularly in Victoria Park, where the largest Lunar New Year Market in Hong Kong is hosted. In this study, we conducted a video-based observational survey to collect pedestrian flow and speed data at the Victoria Park Lunar New Year Market on the eve of the Lunar New Year. Using the collected data, an extant mathematical model was calibrated to capture the relationships between the relevant macroscopic quantities, thereby providing insight into pedestrian behavior at the carnival event. Bayesian inference was employed to calibrate the model by using prior data obtained from a previous controlled experiment. Results obtained enhance our understanding of crowd behavior under different conditions at carnival events, thus facilitating the improvement of the safety and efficiency of similar events in the future.
Today, large quantities of vehicle data (FCD: floating car data) are widely used by traffic service providers to create and broadcast traffic states in road networks. As a first processing step, all raw position data received from Global Positioning Systems (GPS) have to be map matched in a digital road map. The technical aspects of such a matching process for GPS data are described in this report. After the matching process, space-time-diagrams are created of the probe data showing traffic situation details over space and time. Various examples illustrate how traffic service quality depends on the number of matched GPS raw data; it will be stated that when 2% of connected vehicles in the total traffic flow are sending their GPS data in shorter time intervals, a high quality and precise reconstruction of the current traffic phases is achieved. Traffic reconstruction is followed by a translation into traffic information messages, which can be sent and used in vehicle navigation systems for driver information and dynamic route guidance.