Traffic under a microscope

Transport and PlanningCivil Engineering and Geoscience

Big Data kan ons veel verder brengen

Transport & PlanningCivil Engineering and Geoscience

Online Learning Solutions for Freeway Travel Time Prediction

Providing travel time information to travelers on available route alternatives in traffic networks is widely believed to yield positive effects on individual drive behavior and (route/departure time) choice behavior, as well as on collective traffic operations in terms of, for example, overall time savings and—if nothing else—on the reliability of travel times. As such, there is an increasing need for fast and reliable online travel time prediction models. Previous research showed that data-driven approaches such as the state-space neural network (SSNN) are reliable and accurate travel time predictors for freeway routes, which can be used to provide predictive travel time information on, for example, variable message sign panels. In an operational context, the adaptivity of such models is a crucial property. Since travel times are available (and, hence, can be measured) for realized trips only, adapting the parameters (weights) of a data-driven travel time prediction model such as the SSNN is particularly challenging. This paper proposes a new extended Kalman filter (EKF) based online-learning approach, i.e., the online-censored EKF method, which can be applied online and offers improvements over a delayed approach in which learning takes place only as realized travel times are available.Transport and PlanningCivil Engineering and Geoscience

Reliable travel time prediction for freeways: Bridging artificial neural networks and traffic flow theory

Civil Engineering and Geoscience

A generic multi-level framework for microscopic traffic simulation: Theory and an example case in modelling driver distraction

Incorporation of more sophisticated human factors (HF) in mathematical models for driving behavior has become an increasingly popular and important research direction in the last few years. Such models enable us to simulate under which conditions perception errors and risk-taking lead to interactions that result in unsafe traffic conditions and ultimately accidents. In this paper, we present a generic multi-level microscopic traffic modelling and simulation framework that supports this important line of research. In this framework, the driving task is modeled in a multi-layered fashion. At the highest level, we have idealized (collision-free) models for car following and other driving tasks. These models typically contain HF parameters that exogenously “govern the human factor”, such as reaction time, sensitivities to stimuli, desired speed, etc. At the lowest level, we define HF variables (task demand and capacity, awareness) with which we maintain what the information processing costs are of performing driving tasks as well as non-driving related tasks such as distractions. We model these costs using so-called fundamental diagrams of task demand. In between, we define functions that govern the dynamics of the high-level HF parameters with these HF variables as inputs. When total task demand increases beyond task capacity, first awareness may deteriorate, where we use Endsley's three-level awareness construct to differentiate between effects on perception, comprehension, anticipation and reaction time. Secondly, drivers may adapt their response in line with Fullers risk allostasis theory to reduce risk to acceptable levels. This framework can be viewed as a meta model, that provides the analyst possibilities to combine and mix a wide variety of microscopic models for driving behavior at different levels of sophistication, depending on which HF are studied, and which phenomena need to be reproduced. We illustrate the framework with a distraction (rubbernecking) case. Our results show that the framework results in endogenous mechanisms for inter- and intra-driver differences in driving behavior and can generate multiple plausible HF mechanisms to explain the same observable traffic phenomena and congestion patterns that arise due to the distraction. We believe our framework can serve as a valuable tool in testing hypotheses related to the effects of HF on traffic efficiency and traffic safety in a systematic way for both the traffic flow and HF community.Transport and Plannin

DiTTlab: (big) data meets simulatie

De hoogleraren Hans van Lint en Alexander Verbraeck vertellen in deze bijdrage over de propositie van DiTTlab en over de eerste projecten die er worden uitgevoerd.Transport & PlanningCivil Engineering and Geoscience

Empirical analysis of an in-car speed, headway and lane use Advisory system

For a recently developed in-car speed, headway and lane use advisory system, this paper investigates empirically advice validity (advice given in correct traffic circumstances), credibility (advice logical to drivers) and frequency. The system has been developed to optimize traffic flow by giving advice on a tactical scale. This scale allows traffic flow improvement and fills a gap as most ITS which aim to optimize traffic flow operate on the operational or strategic scale. Using log files of the actual system for a period of two weeks, the validity, credibility and frequency of advices is determined. Validity is not guaranteed as the advices are determined based on a predicted traffic state due to data delay and as the advices are based on the predicted traffic state 1 minute in the future. Given that the advisory system is a first in its kind, a new methodology was developed to assess the system, based on the use of virtual trajectories and defining indicators to assess, validity, credibility and frequency. The analysis shows that many advices are indeed valid and credible, but some are not, allowing room for improvement. Advice frequency is found to be reasonable. The analysis also shows that in-car filtering of advices, i.e. merging equal advices, is important to lower the frequency.Transport & PlanningCivil Engineering and Geoscience

Effects of Periodic Location Update Polling Interval on the Reconstructed Origin–Destination Matrix: A Dutch Case Study Using a Data-Driven Method

Global System for Mobile Communications (GSM) data provides valuable insights into travel demand patterns by capturing people's consecutive locations. A major challenge, however, is how the polling interval (PI; the time between consecutive location updates) affects the accuracy in reconstructing the spatio-temporal travel patterns. Longer PIs will lead to lower accuracy and may even miss shorter activities or trips when not properly accounted for. In this paper, we analyze the effects of the PI on the ability to reconstruct an origin–destination (OD) matrix. We also propose and validate a new data-driven method that improves accuracy in case of longer PIs. The new method first learns temporal patterns in activities and trips, based on travel diaries, that are then used to infer activity-travel patterns from the (sparse) GSM traces. Both steps are data-driven thus avoiding any a priori (behavioral, temporal) assumptions. To validate the method we use synthetic data generated from a calibrated agent-based transport model. This gives us ground-truth OD patterns and full experimental control. The analysis results show that with our method it is possible to reliably reconstruct OD matrices even from very small data samples (i.e., travel diaries from a small segment of the population) that contain as little as 1% of the population’s movements. This is promising for real-life applications where the amount of empirical data is also limited.Transport and Plannin

Traffic dynamics: Its impact on the macroscopic fundamental diagram

Literature shows that–under specific conditions–the Macroscopic Fundamental Diagram (MFD) describes a crisp relationship between the average flow (production) and the average density in an entire network. The limiting condition is that traffic conditions must be homogeneous over the whole network. Recent works describe hysteresis effects: systematic deviations from the MFD as a result of loading and unloading.This article proposes a two dimensional generalization of the MFD, the so-called Generalized Macroscopic Fundamental Diagram (GMFD), which relates the average flow to both the average density and the (spatial) inhomogeneity of density. The most important contribution is that we show this is a continuous function, of which the MFD is a projection. Using the GMFD, we can describe the mentioned hysteresis patterns in the MFD. The underlying traffic phenomenon explaining the two dimensional surface described by the GMFD is that congestion concentrates (and subsequently spreads out) around the bottlenecks that oversaturate first. We call this the nucleation effect. Due to this effect, the network flow is not constant for a fixed number of vehicles as predicted by the MFD, but decreases due to local queueing and spill back processes around the congestion “nuclei”. During this build up of congestion, the production hence decreases, which gives the hysteresis effects.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin

Open traffic: A toolbox for traffic research

Open Traffic is an open source software project that provides a transport modeling software environment. While most transport model packages offer ready-to-use modules for end-users, Open Traffic provides open access to a modelling environment for the (further) development of methods and algorithms and enables the sharing, distribution and further development of the implied knowledge. The Open Traffic platform is designed as a modular system which enables users to utilize existing modules and extend the system with new ones. The system supports the development of multi modal and multi scale models by providing a collection of objects that enable the creation of a transport infrastructure and its environment at multiple levels of detail. The definition of the geographical objects aligns to the principles of CityGML, an open standard for geo data that is internationally accepted by the Open Geospatial Consortium. Additional utilities such as a graphical editor and visualizer, in addition with facilities to import data from external sources like Open Streetmap and Esri shape files, enable users to quickly create and demonstrate their use-cases. In this article we present the high level architecture of Open Traffic, its current status, and a first application with the implementation of the micro simulation model MOTUS. Also, the possibilities and requirements to adhere Open Traffic to agent based modelling approaches are explored.Transport and PlanningCivil Engineering and Geoscience