Generating activity based, multi-modal travel demand for SUMO

Abstract

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.

Traffic Simulation with Open-Source and Commercial Traffic Microsimulators: A Case Study

“…One of these extensions, used for the development of this research, is SumoPy [8]. It is made with Python programming language [4] and is contained in a graphical user interface that provides tools with multiple functionalities: SumoPy allows to manage all the transportation data that can be simulated with Sumo [12] as well as deeply analyze the GPS traces [13][14]. A large number of research and application projects have been carried out with Sumo [4], in addition to…”

“…The software and all its tools can be downloaded from the official website. No payment is required; therefore, it is opensource software under the terms of EPL V2 [4,[12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”

“…In addition to the Microscopic level, the Marouter tool allows calculating a Macroscopic assignment. As of Sumo 0.26.0, it is possible to model scenarios at a Mesoscopic level of detail [4,[12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

“…The validation was performed for each mode of transport; eight iterations were performed in Aimsun, as eight iterations are considered to be sufficient to obtain valid results from a traffic simulation model [12][13][14][22][23][24][25][26][27][28][29]. For bicycles, trucks and buses (Table 6), the GEH statistical criteria validated the results, whereas for the light vehicles and motorcycles (Table 7), although validated, not all the criteria obtained 100 % acceptance, they complied with what had been established by the indicator used.…”

Building a Large-Scale Micro-Simulation Transport Scenario Using Big Data

“…Essentially the synthetic population consists of a database of people, each person with its own attributes (e.g., home/work location, activity pattern, vehicle ownerships, preferred mode, and socioeconomic attributes) and a set of feasible mobility plans. A plan describes a door-to-door trip between successive activities and consists of a series of stages, where each stage represents a movement with a single mode of transport [61]. The estimated or effective execution time of plans allows people to choose their optimal mobility solution for their specific activities, including travel modes and routes.…”