Transportmetrica A-Transport Science最新文献

Traffic-light modelling is a complex task, because many factors have to be taken into account. In particular, capturing all traffic flows in one model can significantly complicate the model. Therefore, several realistic features are typically omitted from most models. We introduce a mechanism to include pedestrians and focus on situations where they may block vehicles that get a green light simultaneously. More specifically, we consider a generalisation of the Fixed-Cycle Traffic-Light (FCTL) queue. Our framework allows us to model situations where (part of the) vehicles are blocked, e.g. by pedestrians that block turning traffic and where several vehicles might depart simultaneously, e.g. in case of multiple lanes receiving a green light simultaneously. We rely on probability generating function and complex analysis techniques which are also used to study the regular FCTL queue. We study the effect of several parameters on performance measures such as the mean delay and queue-length distribution.

Pedestrian injury in pedestrian-vehicle crash is significantly related to the driver, pedestrian, vehicle, crash and environment characteristics. Driver’s route familiarity has been found greatly associated with driving behaviours. Two-year pedestrian-vehicle crash data in Yunnan Province were studied to investigate the factors that affect pedestrian-injury severities in crashes with familiar and unfamiliar drivers by employing mixed logit models. Eight variables were found significant only in the familiar driver model. And six variables were found significant only in the unfamiliar driver model. Estimation findings indicate that the factors of early morning and sunny weather condition will be better modelled as random parameters in the model for familiar drivers and the same with the factors of rainy weather condition and afternoon peak in the model for unfamiliar drivers. Some more effective and targeted countermeasures are put forward for familiar drivers, unfamiliar drivers and transportation managers to reduce pedestrians’ injury severities.

Delay is one of the most important traffic signal performance measures. In coordinated networks, understanding the characteristics of vehicle arrivals is important for coordination purposes and to properly estimate delays. When observed on a cyclical basis in real-time, distinctive arrival patterns can lead to similar delays, which may go undetected by contemporary delay models. This study proposes a set of enhancements to the Incremental Queue Accumulation (IQA) delay model to overcome the limitations of current models. Additionally, this study proposes a hybrid signal performance measure that combines delay and arrival patterns to depict signal performance truthfully. The enhancements to IQA are realised through an algorithm for the identification of distinctive vehicle arrival groups based on high-resolution signal and detection data. The results demonstrate that the proposed model provides reliable delay estimates (MAPE score in range 4.3–11.2%) while reporting a number of traffic arrival characteristics that are not available from the benchmarked models.

Integrating complicated travel behaviour mechanisms into transportation studies is necessary for understanding and modelling urban mobility. However, insufficient research has been conducted in this direction, especially when travellers make decisions using different mechanisms. This study develops a data-driven framework to model day-to-day route choice dynamics, in which different interpretable travel decision-making mechanisms and efficient model training algorithms are incorporated. The route choice is estimated following a Dirichlet distribution. By introducing a high-order hidden Markov state model, the framework can detect the routine and sudden changes of the mechanism and apply them accordingly for prediction. We propose a particle-based Markov chain Monte Carlo algorithm to estimate model parameters. As a pioneering work that links transportation data with different behaviour mechanisms, we demonstrate the feasibility of the proposed framework through a numerical example. With more transportation data, the proposed approach could become an attractive alternative to conventional transportation models.

Delivery drones are a disruptive technology that is spurring logistics system change, such as the adoption of urban micro-fulfilment centres (MFCs). In this paper, we develop and implement a two-stage continuum approximation (CA) model of this disruptive system in a geographic information system. The model includes common CA techniques at a local level to minimise cost, and then these local solutions are used in a second stage regional location-allocation multiple knapsack problem. We then compare the drone MFC system to a traditional delivery-by-van system and investigate potential cost or emissions savings by adjusting time-window demand, logistical sprawl, electric van alternatives, and MFC emissions. Furthermore, we conduct a sensitivity analysis to show that uncertainty in demand and effective storage density both significantly influence the number of MFCs selected and benchmark our model against commercial solvers. This methodology may also be further developed and applied to other new delivery vehicle modes.

A speed perturbation model is proposed in this paper for the heterogeneous platoon to measure the interaction between vehicles and investigate the propagation laws of perturbations. Then, a ratio is defined to quantify the relationship between the communicating information impact (CII) and the car-following behaviour impact (CFI). A modified IDM is employed to evaluate the role of communicating information in perturbation propagation. Results show that communicating information can suppress the amplification of perturbation. Under the stable environment, the interaction between vehicles decreases with frequency. There is a critical frequency ${\omega }_c$ (value of 0.55 in this case) that distinguishes the relationship between the CII and CFI. When the frequency is smaller than ${\omega }_c$, the CFI is larger than CII; otherwise, the CII dominates the interactions between vehicles. Under the unstable environment, the interaction between vehicles increases first and decreases then, where the vehicles are mainly affected by the car-following behaviours.

Bike-sharing improves individual mobility, considerably reshaping the landscape of job accessibility and commuting time. Existing empirical studies in urban transportation involving commuting usually collect survey data at the aggregate level. A comprehensive understanding of the influence of bike-sharing on commuting and job accessibility at the city level is still missing in developing countries. Using mobile phone data in Beijing, this study addresses these questions with a commuting mode model and cumulative accessibility model. The results indicate that bike-sharing could lead to a decrease in commuting time and an increase in job accessibility. The availability of bike-sharing services has a positive relationship with its effectiveness. Meanwhile, bike-sharing significantly reduces the horizontal and vertical inequality in commuting time and job accessibility at both the individual and spatial levels. These findings provide insights into the popularity of bike-sharing in China, shed light on the equity influence of bike-sharing, and provide a quantitative measurement of the benefit of bike-sharing.

Emergency Medical Services (EMS) constitute a crucial pillar of today's cities by providing urgent medical responses to their citizens. Their study is often conducted via simulation, as the assessment of planning decisions is generally unfeasible in the existing systems. However, such models can become computationally expensive to run. Thus, metamodels can be used to approximate the simulation results.

In this work, a simulation metamodelling strategy supported on an active learning scheme is proposed to analyse the survival rate of a simulated EMS. The exploration process is guided through a series of grids towards simulation input regions whose output results match a specific survival rate defined a priori. This provides an efficient way of exploring the search space by channelling the computational effort to the most important input values, supporting the advantages of these methodologies in the EMS field, where their application is still seldom to the best of our knowledge.