SUM

Seamless Shared Urban Mobility (SUM)

This software enables the generation of trip requests and the simulation of various travel scenarios within a defined study area. Moreover, it allows the assessment of key performance indicators (KPIs) for the New Shared Mode (NSM) integrated with public transport (PT).

Technology

The software was implemented using Python programming language and its basic libraries

Description

The developed framework utilizes two main frameworks:

• ExMAS
• Public Transport queries

The roadmap of the current project for the city:

1. Create a demand dataset. Sampling is done according to the established travel patterns.

2. Calculate utilities of different transport modes:

a) Utility of PT for trips from O to D: $U_{PT:O\to \overline{D}}$

b) Utility of PT for trips from HUB to D: $U_{PT:HUB\to \overline{D}}$

c) Utility of ride-pooling (following ExMAS) for all PT users from O to HUB:

$$ \begin{aligned} U_{SUM}=U_{PT:HUB\to \overline{D}} + \underbrace{\beta _{t}\beta _{s}\left ( t _{t}+ \beta _{w}t _{w}\right)}+ASC \end{aligned} $$

3. Mode Choice

   • сalculate ASC for the given $E(p_{sum})$
   • define the average ASC
   • recalculate $p_{sum}$ for all PT users

4. Demand for SUM

   • sample travellers choosing SUM as: $p_{random} < p_{sum}$

5. KPIs for SUM

   • ExMAS-calculated KPIs for SUM users from O to HUB
   • assessment

Input:

• csv file with population and address points distribution

• graphml file with city graph

• the default file with configurations

• dbf file with OSM network (available e.g. here)

• zip with GTFS file for the area and date that we query (available e.g. from gtfs)

• both OSM and GTFS files should be stored in the data folder

Output:

• csv with results

Usage:

• Simulations, KPI estimation and comparative analysis of two SUM areas notebook
• running the OTP server notebook