Ihab Kaddoura

Agent-based Congestion Pricing and Transport Routing with Heterogeneous Values of Travel Time Savings

An existing agent-based simulation framework and congestion pricing methodology is extended towards a consistent consideration of non-linear, user- and trip-specific values of travel time savings (VTTS). The heterogeneous VTTS are inherent to the model and result from each agents individual time pressure. An innovative approach is presented which accounts for the non-linear, user- and trip-specific VTTS (i) when converting external delays into congestion tolls and (ii) when generating new transport routes. The innovative pricing and routing methodology is applied to a real-world case study of the Greater Berlin area, Germany. The proposed methodology performs better than assuming a constant value of travel time savings or randomizing the routing relevant costs. The improved consistency of setting congestion toll levels, identifying transport routes and evaluating travel plans is found to result in a higher system welfare.

Simultaneous internalization of traffic congestion and noise exposure costs

This study elaborates on the interrelation of external effects, in particular road traffic congestion and noise. An agent-based simulation framework is used to compute and internalize user-specific external congestion effects and noise exposures. The resulting user equilibrium corresponds to an approximation of the system optimum. For traffic congestion and noise, single objective optimization is compared with multiple objective optimization. The simulation-based optimization approach is applied to the real-world case study of the Greater Berlin area. The results reveal a negative correlation between congestion and noise. Nevertheless, the multiple objective optimization yields a simultaneous reduction in congestion and noise. During peak times, congestion is the more relevant external effect, whereas, during the evening, night and morning, noise is the more relevant externality. Thus, a key element for policy making is to follow a dynamic approach, i.e. to temporally change the incentives. During off-peak times, noise should be reduced by concentrating traffic flows along main roads, i.e. inner-city motorways. In contrast, during peak times, congestion is reduced by shifting transport users from the inner-city motorway to smaller roads which, however, may have an effect on other externalities.

Using real-world traffic incident data in transport modeling

Abstract This study incorporates real-world traffic incident data into a transport simulation and analyzes the impact of roadworks, accidents and other incident types on the transport system. Traffic incidents are modeled as a reduction in road capacity to which transport users can react by adjusting their transport routes. Depending on the type of traffic incident, i.e. long-term vs. short-term effect, a different behavioral reaction is implemented which reflects a different assumption regarding the transport users level of knowledge. Simulation experiments for the Greater Berlin area indicate that traffic incidents cause an increase in average travel time per car trip of 5-7 minutes. Also, over a long period of time, traffic incidents have a significant effect on the transport system: On an average working day, for almost half of all car trips, transport users either travel on a road (segment) which is affected by a traffic incident or bypass such a road (segment). Overall, this study highlights the importance to account for traffic incidents in transport modeling. Accounting for traffic incidents allows to quantify the effects from roadworks, accidents and other incident types. Furthermore, the simulation of traffic incidents makes the model more realistic and allows for an improved policy evaluation and decision-making.

Simulation-based optimization of service areas for pooled ride-hailing operators

Abstract Dynamic ride hailing with passenger pooling has become a popular form of urban transport and is a growing sector around the globe. The area where these services operate is often limited to densely populated inner city districts, whereas non-pooled options are often available in larger areas. In this paper, we introduce a simulation-based methodology that allows to optimize the service area of a ride hailing service using an agent-based simulation and apply it to the taxi demand of Berlin, Germany. Three different criteria are used for the optimization, which take the average vehicle occupancy, the revenues collected per area or both into account. The results show that for the given parameters a service area that focuses on an extended central area and some areas around may be profit-maximizing for operators.