Generalized gap acceptance models for unsignalized intersections

This paper contributes to the modeling and analysis of unsignalized intersections. In classical gap acceptance models vehicles on the minor road accept any gap greater than the CRITICAL gap, and reject gaps below this threshold, where the gap is the time between two subsequent vehicles on the major road. The main contribution of this paper is to develop a series of generalizations of existing models, thus increasing the model's practical applicability significantly. First, we incorporate {driver impatience behavior} while allowing for a realistic merging behavior; we do so by distinguishing between the critical gap and the merging time, thus allowing MULTIPLE vehicles to use a sufficiently large gap. Incorporating this feature is particularly challenging in models with driver impatience. Secondly, we allow for multiple classes of gap acceptance behavior, enabling us to distinguish between different driver types and/or different vehicle types. Thirdly, we use the novel M$^X$/SM2/1 queueing model, which has batch arrivals, dependent service times, and a different service-time distribution for vehicles arriving in an empty queue on the minor road (where `service time' refers to the time required to find a sufficiently large gap). This setup facilitates the analysis of the service-time distribution of an arbitrary vehicle on the minor road and of the queue length on the minor road. In particular, we can compute the MEAN service time, thus enabling the evaluation of the capacity for the minor road vehicles

Fast Long-Term Multi-Scenario Prediction for Maneuver Planning at Unsignalized Intersections

Motion prediction for intelligent vehicles typically focuses on estimating the most probable future evolutions of a traffic scenario. Estimating the gap acceptance, i.e., whether a vehicle merges or crosses before another vehicle with the right of way, is often handled implicitly in the prediction. However, an infrastructure-based maneuver planning can assign artificial priorities between cooperative vehicles, so it needs to evaluate many more potential scenarios. Additionally, the prediction horizon has to be long enough to assess the impact of a maneuver. We, therefore, present a novel long-term prediction approach handling the gap acceptance estimation and the velocity prediction in two separate stages. Thereby, the behavior of regular vehicles as well as priority assignments of cooperative vehicles can be considered. We train both stages on real-world traffic observations to achieve realistic prediction results. Our method has a competitive accuracy and is fast enough to predict a multitude of scenarios in a short time, making it suitable to be used in a maneuver planning framework

An empirical delay model for application in unsignalized intersections in dynamic traffic assignment

textUp until recently, unsignalized nodes have been either ignored or inadequately represented in Dynamic Traffic Assignment (DTA) models. This is due to the difficult nature of incorporating internal node conflicts into dynamic flow models. It was thought or assumed that these nodes had little impact on overall model results, but evidence from testing in Visual Interactive System for Transportation Algorithms (VISTA), a DTA model, reveals that may not be the case. This paper explores recent attempts at characterizing stop sign effects within DTA flow models. From previous studies, it has been found that incorporating these unsignalized and priority movements internal to the flow model requires large amounts of computational power, are challenging to make efficient, and lead to a multiple or infinite solution space. Based on these findings, a deterministic approach is both impractical and likely impossible in the existing framework of the Cell Transmission (CTM) and Link Transmission (LTM) models commonly used in DTA. Thus, a method of utilizing empirical relationships based on information readily available in these models may be a more acceptable approach. Microsimulation is much more suitable for modeling these types of interactions and is capable of producing results near to reality. For this reason, microsimulation was chosen as a viable method for developing empirical relationships of such complex interactions to then be used as inputs into the macroscopic flow models of DTA. This paper presents a model developed to calculate delays expected by vehicles at stop approaches based on information that can be taken from a dynamic flow model such as CTM and LTM models. This model is validated by video data recorded and analyzed for accuracy. Potential uses and probable implementations of the model are explored to appropriately incorporate unsignalized and priority movements into existing flow models.Civil, Architectural, and Environmental Engineerin

Modeling and Simulation of Cascading Failures in Transportation Systems During Hurricane Evacuations

Effective and timely evacuation is critical in alleviating the impact of hurricanes. As such, evacuation models are often sought to support the preparedness of evacuations. One important task in the modeling process is to evaluate exogenous factors that cause transportation system capacity loss during evacuation. Typical factors include direct damage to the roadway network due to storm surge and cascading impacts because of other facilities failures. For example, power outage can lead to signal failure and subway suspension. This paper aims to develop a macroscopic simulation-based approach to study the capacity loss of the roadway network in evacuation due to signal loss as a consequence of power outage. In particular, to simulate the case in which traffic signals lose power, a capacity-reduction model from signalized intersections to unsignalized (all-way stop control) intersections was developed and calibrated using microscopic model created in SUMO and Synchro. We used the downtown Manhattan as a case study area and created a hypothetical power-grid network in terms of neighborhoods. Six scenarios were built to simulate power loss of different neighborhoods. The simulation results give insights on how cascading failures of power network affect roadway network and evacuation process

Gap acceptance for left turns from the major road at unsignalized intersections

This paper attempts to identify factors that may influence the gap acceptance behavior of drivers who turn left from the major road at unsignalized intersections. Drivers’ accepted and rejected gaps as well as their age and gender were collected at six unsignalized intersections with both two and four lanes on the major road, with and without the presence of a Left-Turn Lane (LTL), and with both high and low Speed Limits (SLs). Whether or not a driver accepts a given gap was considered as a binary decision and correlated logit models were used to estimate the probability of accepting a gap. Models with different factors were tested and the best model was selected by the quasi-likelihood information criterion. The gap duration, the number of rejected gaps, the mean and total time interval of the rejected gaps and the gender of the driver were all significant in explaining the variation of the gap acceptance probability, whereas the number of lanes of the major road, the presence of LTL, the SL and the driver’s age category were not. Gap acceptance probability functions were determined based on the best model, including both the factors of the number of rejected gaps and the mean time interval of the rejected gaps. As the values of these two factors increase, the probability of accepting a given gap rises up. The developed model can be further applied in practice to improve the analysis of traffic operations and capacity at unsignalized intersections. First published online: 10 Jul 201

Estimating traffic operations at multi-lane roundabouts: a case study

This paper addresses traffic modeling issues at urban multi-lane roundabouts where, despite circulating vehicles have priority, negotiation of the right-of-way can occur between antagonist traffic flows, as a result of minor drivers’ failing to obey the nominal operating rule (stop or yield control). Existing models for the estimation of operational performances have the shortcoming of not representing the interdependencies between entering and circulating vehicles at multi-lane roundabouts. An analytical capacity model derived from field observations was developed for this kind of intersections in a previous study. The complexity of the model lies in the difficulty of observing the behavioral parameters which are needed to implement the model. A procedure to get unknown behavioral parameters from traffic surveys is here proposed. This concerns saturation headways, often eluding direct observations due to rare occurrences of traffic conditions in which they can be observed. The unknown parameters were estimated through a regression model using on field data collected at a multi-lane roundabout. The presence of data correlation within a cluster of observations required the estimation of the regression parameters through a generalized estimating equation model. Results gave insight into the analysis of operations at multi-lane roundabouts, containing evidence to support assumptions made for the estimation of unobservable parameters

New Analytic Solutions of Queueing System for Shared-Short Lanes at Unsignalized Intersections

Designing the crossroads capacity is a prerequisite for achieving a high level of service with the same sustainability in stochastic traffic flow. Also, modeling of crossroad capacity can influence on balancing (symmetry) of traffic flow. Loss of priority in a left turn and optimal dimensioning of shared-short line is one of the permanent problems at intersections. A shared-short lane for taking a left turn from a priority direction at unsignalized intersections with a homogenous traffic flow and heterogeneous demands is a two-phase queueing system requiring a first in-first out (FIFO) service discipline and single-server service facility. The first phase (short lane) of the system is the queueing system M(p lambda)/M(mu)/1/infinity, whereas the second phase (shared lane) is a system with a binomial distribution service. In this research, we explicitly derive the probability of the state of a queueing system with a short lane of a finite capacity for taking a left turn and shared lane of infinite capacity. The presented formulas are under the presumption that the system is Markovian, i.e., the vehicle arrivals in both the minor and major streams are distributed according to the Poisson law, and that the service of the vehicles is exponentially distributed. Complex recursive operations in the two-phase queueing system are explained and solved in manuscript