Memory properties and fractional integration in transportation time-series

In transportation analyses, autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH) models have been widely used mainly because of their well established theoretical foundation and ease of application. However, they lack the ability to capture long memory properties and do not jointly treat the mean and variance (variability) of a time-series. We employ fractionally integrated dual memory models and compare results to classical time-series models in a traffic engineering context. Results indicate that dual memory models offer better representation of the original time-series than classical models; further, forcing the differentiation parameter of ARIMA model to equal 1 leads to over-inflated moving average terms and, consequently, to questionable models with artificial correlation structures.     

Which factors lead to driving errors? A structural equation model analysis through a driving simulator experiment

As driving error is a main contributory factor of road accidents, its causes and consequences are of great interest in the road safety decision making process. This paper investigates several factors (including driver distraction, driver characteristics and road environment) that affect overall driving error behaviour and estimates a new unobserved variable which underlines driving errors. This estimation is performed with data obtained from a driving simulation experiment in which 95 participants covering all ages were asked to drive under different types of distraction (no distraction, conversation with passenger, cell phone use) in rural and urban road environment, as well as in both low and high traffic conditions. Driving error was then modeled as a latent variable based on several individual driving simulator parameters. Subsequently, the impact of several risk factors such as distraction, driver characteristics as well as road environment on driving error were estimated directly. The results of this complex model reveal that the impact of driver characteristics and area type are the only statistically significant factors affecting the probability of driving errors. Interestingly, neither conversing with a passenger nor talking on the cell phone have a statistically significant impact on driving error behaviour which highlights the importance of the present analysis and more specifically the development of a measure that represents overall driving error behaviour instead of individual driving errors variables.     

A Real-Time Parking Prediction System for Smart Cities

A methodological framework for multiple steps ahead parking availability prediction is presented. Two different types of predictions are provided: the probability of a free space to continue being free in subsequent time intervals, and the short-term parking occupancy prediction in selected regions of an urban road network. The available data come from a wide network of on-street parking sensors in the “smart” city of Santander, Spain. The sensor network is segmented in four different regions, and then survival and neural network models are developed for each region separately. Findings show that the Weibull parametric models best describe the probability of a parking space to continue to be free in the forthcoming time intervals. Moreover, simple genetically optimized multilayer perceptrons accurately predict region parking occupancy rates up to 30 minutes in the future by exploiting 1-minute data. Finally, the real time, Web-based, implementation of the proposed parking prediction availability system is presented.     

Statistical characteristics of transitional queue conditions in signalized arterials

The performance of signalized arterials is related to queuing phenomena. The paper investigates the effect of transitional traffic flow conditions imposed by the formation and dissipation of queues. A cross-recurrence quantification analysis combined with Bayesian augmented networks are implemented to reveal the prevailing statistical characteristics of the short-term traffic flow patterns under the effect of transitional queue conditions. Results indicate that transitions between free-flow conditions, critical queue conditions that exceed the detector’s length, as well as the occurrence of spillovers impose a set of prevailing traffic flow patterns with different statistical characteristics with respect to determinism, nonlinearity, non-stationarity and laminarity. The complexity in critical queue conditions is further investigated by introducing two supplementary regions in the critical area before spillover occurrence. Results indicate that the supplementary information on the transitional conditions in the critical area increases the accuracy of the predictive relations between the statistical characteristics of traffic flow evolution and the occurrence of transitions.     

Integrated population synthesis and workplace assignment using an efficient optimization-based person-household matching method

Transportation - Large scale activity-based simulation models inform a variety of transportation and planning policies using models that often rely on fixed or flexible workplace location in a...     

A real-time signal control strategy for mitigating the impact of bus stops at urban signalized intersections

Buses stopping at transit stops reduce the capacity of signalized intersections, which can lead to excessive delays for all users. In order to avoid such phenomena, signal control strategies can be utilized. This article presents a real-time signal control strategy to mitigate the impact of bus stop operations on traffic operations along an undersaturated approach. The objective of the proposed strategy is to ensure that any residual queues due to a bus stopping are fully dissipated as soon as possible by increasing the green time for the bus stop approach as soon as the bus departs. In addition, this strategy ensures that the cross-street approaches do not become oversaturated. Kinematic wave theory is utilized to track the formation and dissipation of queues and determine the green extension (or red truncation) for the subject approach. The benefits achieved from the proposed strategy are illustrated through simulation tests at a single intersection for a variety of bus stop and bus operation characteristics. Average delay and average queue length for the bus stop and cross-street approaches, as well as for the whole intersection, are used to assess the performance of the strategy. The tests performed indicate that the signal control strategy can achieve substantial reductions in delay for the bus stop approaches and the intersection as a whole without adversely affecting the cross street operations, when the demand at those cross streets is low.     

Short‐term traffic forecasting: Overview of objectives and methods

In the last two decades, the growing need for short‐term prediction of traffic parameters embedded in a real‐time intelligent transportation systems environment has led to the development of a vast number of forecasting algorithms. Despite this, there is still not a clear view about the various requirements involved in modelling. This field of research was examined by disaggregating the process of developing short‐term traffic forecasting algorithms into three essential clusters: the determination of the scope, the conceptual process of specifying the output and the process of modelling, which includes several decisions concerning the selection of the proper methodological approach, the type of input and output data used, and the quality of the data. A critical discussion clarifies several interactions between the above and results in a logical flow that can be used as a framework for developing short‐term traffic forecasting models.