Towards a Data-rich Paradigm of Public Transport Planning

The main aim of this project is to develop the fundamental principles for a new transportation planning paradigm based on big data sources. In this project we will focus on public transport (PT) as nowadays there is easier access to public open data sources and in addition a strong motivation for PT improvement from a sustainability policy perspective.
The main objectives of the project are:

  • To develop robust methods for extracting meaningful information for improving PT planning by mining available open big data sources;
  • To test the added value of these methods in a case study for analyzing the justification for dedicated
    bus lanes (DBLs) in the City of Beer Sheva (and possibly Tel Aviv if data is available).

Tessellation of Urban Parking Prices

Cruising for parking causes major externalities in urban areas exacerbating traffic congestion, air and noise pollution. A main reason why drivers keep on cruising is the mismatch between parking demand and its supply in central areas. Cities are attempting to resolve this problem by establishing parking limitations and getting the parking prices right – increasing them in the congested areas and decreasing where free spaces are more available in order to preserve a parking occupation level at 85-93%. San Francisco achieved this goal, but within a scope of a year and with a costly $18M price tag to taxpayers. The SF tessellation of parking space is based on link-
differing prices that are difficult for both drivers and city officials to work with. Accordingly, finding a systemic methodology for the tessellation of parking prices that are easily comprehended by drivers, motivates changes in their parking behavior and can be feasibly applicable in any city is both lacking and timely.

Our research breaks new ground by developing and implementing a behaviorally salient and workable methodology to establish the spatiotemporal tessellation of parking prices within the natural dynamics of a complex urban-transportation system. To this aim we develop serious games of competitive parking search to understand and estimate drivers’ search behavior in reaction to price changes, algorithms of pricing and combine them with the state-of-the-art algorithms of spatial tessellation,
accounting for the high heterogeneity in urban parking demand and supply. The proposed approach is employed and tested within spatially-explicit agent-based and game-based models of urban parking search in order to account for the bounded rationality of drivers parking behavior.
The models and entire framework are calibrated and validated in field studies and investigated in real-world scenarios in selected case studies in Israel and Belgium. Based on these studies we propose scenarios that aim at seamless transition to the optimal tessellation of parking space. The project will provide an operational model for managing the critical parking subsystem of the greater urban-transportation system thus advancing the theory of socially-driven complex systems. Its results will enable formulating a robust policy framework for implementing parking pricing tessellation that is accepted by drivers and stakeholders and easily translated to any city, provided the necessary GIS databases are available.

Mobility as a Service: From Rigid to Smart Evolving Public Transport

Efficient mobility is vital for the smart city. To guarantee high accessibility levels in the future smart city dwellers we propose an evolutionary ICT-enabled pathway from the current rigid to a flexible public transport (PT) network. This pathway is based on the concept of Mobility as a Service (MaaS) that aims at seamless integration of all types of mobility services for a smooth multimodal journey and on a novel evolutionary approach of public transport network design and operational restructuring of the traditional components of the PT network – buses, metro, and light rail. To reach this ambitious goal, we plan to mine vast amounts of travel trajectory data generated by mobile phone call data records (CDRs) and PT smartcards at a high resolution of individual travelers, PT lines and stops. These data provide spatially- explicit insights on where, when, and how people are traveling over weeks and months.
The specific goals of our research are:

  1. To develop a salient methodology for the analysis of spatially-explicit data on individual- level mobility supplied by mobile telephony and smartcards and recognizing the existing and emerging spatiotemporal travelers’ flows and their accessibility gaps.
  2. To develop algorithms of adaptive evolution of the PT network aiming at closing the revealed and expected accessibility gaps and to propose the techniques of seamless adaptation of the PT network to the changing demand via modification of routes, timetables, and vehicles as well as offering demand-responsive services.
  3. To suggest robust and seamless policy pathways for the strategic implementation of the Mobility as a Service (MaaS) concept in smart city architectures.
  4. To evaluate the methodology and algorithms in applications for two metropolitan area case studies – Tel-Aviv (IL) and Shenzhen (CN), and compare between two very different
    cultural contexts and geographical scales

MaaS does not demand abrupt changes to existing networks and can be thus introduced with social and political consensus in a win-win way, for the operator/planner as well as for the traveler. When implemented at a larger scale MaaS and adaptive PT will increase dramatically the attractiveness of PT relative to single-occupant automobiles (private or shared), fostering the creation of a smart, safe, user-friendly, and efficient cross-modal transportation system without the need to curb personal mobility. The proposal is based on the most recent achievements of the Chinese and Israeli teams in regards to the analysis of big data on human personal mobility (mobile phone and smartcard data) and Public Transport Network Design (PTND) algorithms. The teams will share their
algorithms and software on PTND (Israeli team), data analysis (Chinese team) and apply them in two complementary study cases of Tel-Aviv and Shenzhen metropolitan areas, where they already have established rich spatially-enabled databases of the transportation infrastructures
and human mobility