Dali Wei

Vehicle delay estimation at unsignalised pedestrian crosswalks with probabilistic yielding behaviour

Unsignalised pedestrian crosswalks are commonly adopted in school and residential areas. To enhance pedestrian safety, various types of signs and crosswalk markings have been implemented, which results in motorists’ probabilistic yielding behaviour and interrupted traffic flow patterns. Predicting the vehicular delay is of central importance to evaluate the level of service. However, as the interaction involves two random streams and is governed by the uncertain yielding behaviour, the analysis could be fairly challenging. In this paper, a novel method is proposed to estimate vehicular delay, which decomposes the vehicular stream into free-flow and queuing traffic. By explicitly considering the relation between the vehicular headway and the critical gap, the probability of a yielding event is derived to the expected proportion of queue formation, queue dispersion and free-flow periods. Equations of the average vehicular delay are given as a function of the vehicle volume, pedestrian volume and the yielding rate. The validation experiment using a stochastic simulation indicates that the proposed method consistently gives close estimations with absolute error less than 1 s.

Effects of Demographic and Driver Factors on Single-Vehicle and Multivehicle Fatal Crashes: Investigation with Multinomial Logistic Regression

Human error is often considered the leading cause of motor vehicle crashes. Although some research has been conducted to assess the influence of human factors, full driver impacts on crashes are rarely analyzed, especially on a large scale in the United States. This study sought to identify the driver behavior and demographic factors that affected the likelihood of a multivehicle or single-vehicle fatal crash. A multinomial logistic regression framework, including odds ratios, was used to analyze the variables from several states. A tiered model approach was adopted to find the variable effects for combined, urban, rural, undivided urban, divided urban, undivided rural, and divided rural data sets. Each model produced different significant demographic or driver variables, many being unique or contradictory to the expected results of other research. Gender, often seen as a major contribution to crash outcome, was significant only for the full and urban models and likely not an important variable for determining crash outcomes in rural areas. Distracted driving and failing to make avoidance maneuvers were notably significant across various roadway types. Contrary to other studies, age, licensure, restraint use, and driving at certain times of the day were not found to be significant factors for either single- or multivehicle fatal crashes. Last, some previous conclusions about the number of occupants were refuted. These results may help safety analysts improve crash analysis and prevention methods.

Traffic queuing at unsignalized crosswalks with probabilistic priority

Probabilistic yielding behavior is often observed at unsignalized crosswalks, but its impacts on the traffic flow in terms of traffic capacity and associated vehicular delay have not been examined before. The uniqueness of this problem is that neither traffic nor pedestrian flow holds the absolute priority as normally assumed in existing literature. Based on queuing theory, this paper developed traffic capacity and delay formulas considering the probabilistic priority. The proposed capacity equation reduces to the classic formula when the yielding rate equals one. The service time distributions for both queuers and non-queuers were derived, and M/G2/1 queuing model was applied to determine queue length and vehicular delay. Stochastic simulations showed that the proposed capacity and delay formulas precisely match the simulation results. For ease of practical applications, simpler queuing formulas including M/G/1, M/M/1 and M/D/1 were also examined. Noting that the differences between M/G2/1 and M/G/1 model are only marginal, especially for low yielding rates, we recommend the M/G/1 model for practical applications. In addition, when the pedestrian volume is relatively low, the M/M/1 model is also applicable due to its sufficient accuracy and simplicity.

Modeling and Analyzing Taxi Congestion Premium in Congested Cities

Traffic congestion is a significant problem in many major cities. Getting stuck in traffic, the mileage per unit time that a taxicab travels will decline significantly. Congestion premium (or so-called low-speed fare) has become an increasingly important income source for taxi drivers. However, the impact of congestion premium on the taxicab market is not widely understood yet. In particular, modeling and analyzing of the taxi fare structure with congestion premium are extremely limited. In this paper, we developed a taxi price equilibrium model, in which the adjustment mechanism of congestion premium on optimizing the taxi driver’s income, balancing the supply and demand, and eventually improving the level of service in the whole taxicab market was investigated. In the final part, we provided a case study to demonstrate the feasibility of the proposed model. The results indicated that the current taxi fare scheme in Beijing is suboptimal, since the gain from the raise of congestion premium cannot compensate for the loss from the demand reduction. Conversely, the optimal fare scheme suggested by our model can effectively reduce the excessive demand and reach the supply-demand equilibrium, while keeping the stability of the driver’s income to the maximum extent.