Igor Dakic

Designing Dynamic Delivery Parking Spots in Urban Areas to Reduce Traffic Disruptions

Pick-up and delivery services are essential for businesses in urban areas. However, due to the limited space in city centers, it might be unfeasible to provide sufficient loading/unloading spots. As a result, this type of operations often interferes with traffic by occupying road space (e.g., illegal parking). In this study, a potential solution is investigated: Dynamic Delivery Parking Spots (DDPS). With this concept, based on the time-varying traffic demand, the area allowed for delivery parking changes over time in order to maximize delivery opportunities while reducing traffic disruptions. Using the hydrodynamic theory of traffic flow, we analyze the traffic discharging rate on an urban link with DDPS. In comparison to the situation without delivery parking, the results show that although DDPS occupy some space on a driving lane, it is possible to keep the delay at a local level, that is, without spreading to the network. In this paper, we provide a methodology for the DDPS design, so that the delivery requests can be satisfied while their negative impacts on traffic are reduced. A simulation study is used to validate the model and to estimate delay compared to real situations with illegal parking, showing that DDPS can reduce system’s delay.

Backpressure Traffic Control Algorithms in Field-like Signal Operations

Traffic signal control is one of the most common means of traffic management in urban areas. To create an efficient urban transportation network, the optimization of signal control strategy is required. Various methods and tools can be used for that purpose. This study proposes two signal control algorithms that are based on backpressure model, which is originally developed to maximize the throughput in communication networks. Thus, one of the goals was to determine if such control strategies can lead to maximum throughput through an urban traffic network. In addition, the evaluation of the two algorithms included comparison of their performances with the performances of the conventional signal control strategies in microsimulation software. Evaluation results, in terms of various performance measures, demonstrate that backpressure control models are outperformed by conventional (fixed and actuated) signal timings optimized by a genetic algorithm.