Assessment of multi class kinematic wave models

In the last decade many multi class kinematic wave (MCKW) traffic ow models have been proposed. MCKW models introduce heterogeneity among vehicles and drivers. For example, they take into account differences in (maximum) velocities and driving style. Nevertheless, the models are macroscopic and the ow is modeled as a continuum flow, without...

Data collection by serious gaming

Multi-scale traffic flow theory and modelling

Fastlane: Traffic flow modeling and multi-class dynamic traffic management

Dynamic Traffic Management (DTM) aims to improve traffic conditions. DTM usually consists of two steps: first the current traffic is estimated, then appropriate control actions are determined based on that estimate. In order to estimate and control the traffic, a suitable traffic flow model that reproduces the properties of traffic well must be...

Relationship between application scale and maximum time latency in intelligent transport solutions

Congestion is a major problem in large urbanised areas. Intelligent Transport Solutions aim to reduce this problem. Generally, traffic is monitored using sensors, this data is processed, a traffic state is estimated and a control measure is computed and implemented. The availability and quality of the data and the processing time of the...

Everscape: The Making of a Disaster Evacuation Experience

Disaster evacuation studies are important but difficult or impossible to conduct in the real world. Evacuation simulation in a virtual world can be an important tool to obtain data on the escape and choice behavior of people. However, to obtain accurate “realistic” data, the engagement of participants is a key challenge. Therefore, we describe...

Routing strategies based on the macroscopic fundamental diagram

An excess number of vehicles in a traffic network will reduce traffic performance. This reduction can be avoided by traffic management. In particular, traffic can be routed such that the bottlenecks are not oversaturated. The macroscopic fundamental diagram provides the relation between the number of vehicles and the network performance. One can...

Vehicle-class Specific Route-guidance of Freeway Traffic by Model-predictive Control

Few Active Traffic Management measures proposed in the past consider the distinction of different vehicle classes. Examples of vehicle-class specific measures are truck lanes and high-occupancy/toll (HOT) lanes. We propose that the distinction of different vehicle classes, with different flow characteristics and societal and economic function,...

Data requirements for traffic control on a macroscopic level

With current techniques, traffic monitoring and control is a data intensive process. Network control on a higher level, using high level variables, can make this process less data demanding. The macroscopic fundamental diagram relates accumulation, i.e. the number of vehicles in an area, to the network performance, but only holds for situations...

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...

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

Robust freeway travel time prediction with state-space neural networks, a recurrent neural network approach