Real time traffic models, decision support for traffic management

Reliable and accurate short-term traffic state prediction can improve the performance of real-time traffic management systems significantly. Using this short-time prediction based on current measurements delivered by advanced surveillance systems will support decision-making processes on various control strategies and enhance the performance of the overall network. By taking proactive action deploying traffic management measures, congestion may be prevented or its effects limited. An approach of short-term traffic state prediction is presented and implemented in a real life case for the city of Assen in the Netherlands. This prediction is based on connecting online traffic measurements with a real time traffic model using the macroscopic dynamic traffic assignment model StreamLine in a rolling horizon implementation. Different monitoring data sources consisting of both fixed-point and floating car data are used. The advantage of the rolling horizon approach is that no warming-up period is needed for the dynamic traffic assignment taking less computation time while keeping results consistent. Further, the current traffic state estimation is done by combining model estimates of previous predictions and current measurements. The results of predictions made in the real life case are presented as well as several tested methods for improving the current state estimations showing promising results

De meerwaarde van dynamisch rekenen aan emissies. (The added value of dynamic calculations of emissions)

Het statisch rekenen aan emissies kan tot significante fouten leiden in het ex-ante evalueren van maatregelen en uiteindelijk ook toetsing aan normen. Om dit te onderbouwen zijn het statisch rekenen en dynamisch rekenen aan emissies vergeleken in een case studie op de A12 zowel voor een basissituatie als bij het nemen van infrastructurele maatregelen

Combining new data gathering technology to investigate pedestrian movements in cities

Little is known about pedestrian movements, densities and presence in city centers, although this knowledge could improve city planning, design of infrastructure and management of traffic flows. New data sources available due to fast developments in information and communication technology offer new possibilities for monitoring. Knowledge on pedestrian movements in city centers is essential for adequate city planning, road design, traffic management and security. As the city of Amsterdam has to deal with urban density, crowding and security problems, a case study named “the Red Carpet” was conducted, in which various pedestrian sensing technologies were tested simultaneously. The study area “Red Carpet” lies within the city center of Amsterdam. The “Red Carpet” study focused on the movement of pedestrians between the Central Station and the Reguliersbreestraat vice versa. The municipality of Amsterdam formulated specific research questions regarding the movement of pedestrians between certain locations within the study area, which formed the basis for the installation of sensing technology in the area. The duration of the pilot was nearly 3 months at the end of 2014. Questions regarding the movement of pedestrians in the city center (i.e. their numbers and routes) and additional characteristics like origin, number of foreigners, frequency of visits, activities and trip duration were addressed. Four sensing technologies were deployed: (1) WiFi, (2) a dedicated mobility app using GPS, (3) GSM (mobile phone data) and (4) a smart camera. This paper presents and discusses the results and possibilities of the various sensing technologies. The results show the potential of getting insights in pedestrian movements and that the sources can provide complementary information. However, although the device generated data can provide distributions of pedestrian movement. Possible biases in representation and expansion are important aspects when these data sources are used especially regarding unique visitors. The GPS data was analyzed but turned out not to be representative. The results which were representative show that Saturday is the busiest day in pedestrian movements in the City and that these are mainly foreign visitors. The results of the pilot will be used for developing a policy framework on pedestrians and crowd management

Acceleration of solving the dynamic multi-objective network design problem using response surface methods

Optimization of externalities and accessibility using dynamic traffic management measures on a strategic level is a specific example of solving a multi-objective network design problem. Solving this optimization problem is time consuming, because heuristics like evolutionary multi objective algorithms are needed and solving the lower level requires solving the dynamic user equilibrium problem. Using function approximation like response surface methods (RSM) in combination with evolutionary algorithms could accelerate the determination of the Pareto optimal set. Three algorithms in which RSM are used in different ways in combination with the Strength Pareto Evolutionary Algorithm 2+ (SPEA2+) are compared with employing the SPEA2+ without the use of these methods. The results show that the algorithms using RSM methods accelerate the search considerably at the start, but tend to converge more quickly, possibly to a local optimum, and therefore loose their head start. Therefore, usage of function approximation is mainly of interest if a limited number of exact evaluations can be done or this can be used as a pre phase in a hybrid approach