A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources.

This research describes numerical methods to analyze the absolute transport demand of cyclists and to quantify the road network weaknesses of a city with the aim to identify infrastructure improvements in favor of cyclists. The methods are based on a combination of bicycle counts and map-matched GPS traces. The methods are demonstrated with data from the city of Bologna, Italy: approximately 27,500 GPS traces from cyclists were recorded over a period of one month on a volunteer basis using a smartphone application. One method estimates absolute, city-wide bicycle flows by scaling map-matched bicycle flows of the entire network to manual and instrumental bicycle counts at the main bikeways of the city. As there is a fairly high correlation between the two sources of flow data, the absolute bike-flows of the entire network have been correctly estimated. Another method describes a novel, total deviation metric per link which quantifies for each network edge the total deviation generated for cyclists in terms of extra distances traveled with respect to the shortest possible route. The deviations are accepted by cyclists either to avoid unpleasant road attributes along the shortest route or to experience more favorable road attributes along the chosen route. The total deviation metric indicates to the planner which road links are contributing most to the total deviation of all cyclists. In this way, repellant and attractive road attributes for cyclists can be identified. This is why the total deviation metric is of practical help to prioritize bike infrastructure construction on individual road network links. Finally, the map-matched traces allow the calibration of a discrete choice model between two route alternatives, considering distance, share of exclusive bikeway, and share of low-priority roads.

This article explains a travel demand generator developed within the SUMOPy framework which aims at providing person-based plans for the SUMO micro-simulator. The plan generation has four principal steps: 1.) a population needs to be generated, with specific attributes for each person; 2.) activities and their associated locations need to be identified, 3.) travel plans need to be generated, with the aim to connect the various activities in an efficient manner. 4.) A microsimulator determines the effective travel times for each plan which persons can use to modify or change their plan. In a first part, this article briefly describes other software packages which allow activity based demand models. It is further explained that the use of SUMO as microsimulator is particularly suited to evaluate multi-modal travel plans.The article then focuses on SUMOPy's activity based demand model and in particular on the population synthesizer, plan generation and plan selection step. SUMOPy's activity based demand framework has two distinguishing features: 1.) the time travel budget can be controlled during the population synthesizing process; 2.) The concept of abstract mobility strategies -each person may have different feasible plans, following different mobility strategies. The SUMO micro-simulator is used to evaluate the effective travel time of plans for the entire population. Regarding the plan selection method, a method is described if and how persons change plans based on the the effective travel times experienced after each simulation run. It is shown by means of a synthetic network and a realistic city network that the proposed algorithm is converging and total travel times are decreasing after each simulation run until an equilibrium is reached. Some preliminary attempts were undertaken to improve the speed of convergence. For both of the analyzed networks an equilibrium has been reached after approximately 10 simulation runs.

No abstract

No abstract