Data-driven management of dynamic public transport

Abstract Background A significant mode shift will be required in order to meet the ambitious greenhouse gas emissions reduction targets in Germany and elsewhere. Such a mode shift can only be achieved by a combination of drastic push and pull measures. Getting commuters to switch modes might be particularly difficult and have a negative impact on their access to employment and welfare. Methodology We investigate the potential for a mode shift from car to public transport for German commuters using a data-driven approach based mainly on open data sources that avoids complex transport model runs. Different datasets on the home and workplace location of all employees in Germany are consolidated to create an origin-destination commuter matrix at traffic analysis zone level. The commuter matrix is merged with travel time data for car and public transport to calculate a spatially disaggregated and mode-specific measure of accessibility. The comparison of accessibility by car and public transport is used to derive the potential for a mode shift and identify potential challenges and barriers. Results Public transport accessibility to workplaces is poorer across the country compared to access by car. On average, public transport travel times are almost three times higher than the corresponding car travel times. The differences in accessibility are largely independent of the region type. Results are validated by an independent dataset from a household travel survey. Based on these results, the potential for a mode shift appears to be very low.

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A data-driven approach to help understanding the preferences of public transport users

2017 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata.2017.8258138 ◽

2017 ◽

Cited By ~ 3

Author(s):

Vasco Furtado ◽

Elizabeth Furtado ◽

Carlos Caminha ◽

Andre Lopes ◽

Victor Dantas ◽

...

Keyword(s):

Public Transport ◽

Data Driven ◽

Data Driven Approach

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How Policies on Restaurants, Bars, Nightclubs, Masks, Schools, and Travel Influenced Swiss COVID-19 Reproduction Ratios

10.1101/2020.10.11.20210641 ◽

2020 ◽

Author(s):

C. K. Sruthi ◽

Malay Ranjan Biswal ◽

Brijesh Saraswat ◽

Himanshu Joshi ◽

Meher K. Prakash

Keyword(s):

Machine Learning ◽

Secondary Schools ◽

Public Transport ◽

Scientific Basis ◽

Data Driven ◽

Economic Activities ◽

Model Framework ◽

Fine Grained ◽

Learning Framework

SummaryThe role of complete lockdowns in reducing the reproduction ratios (Rt) of COVID-19 is now established. However, the persisting reality in many countries is no longer a complete lockdown, but restrictions of varying degrees using different choices of Non-pharmaceutical interaction (NPI) policies. A scientific basis for understanding the effectiveness of these graded NPI policies in reducing the Rt is urgently needed to address the concerns on personal liberties and economic activities. In this work, we develop a systematic relation between the degrees of NPIs implemented by the 26 cantons in Switzerland during March 9 – September 13 and their respective contributions to the Rt. Using a machine learning framework, we find that Rt which should ideally be lower than 1.0, has significant contributions in the post-lockdown scenario from the different activities - restaurants (0.0523 (CI. 0.0517-0.0528)), bars (0.030 (CI. 0.029-0.030)), and nightclubs (0.154 (CI. 0.154-0.156)). Activities which keep the land-borders open (0.177 (CI. 0.175-0.178)), and tourism related activities contributed comparably 0.177 (CI. 0.175-0.178). However, international flights with a quarantine did not add further to the Rt of the cantons. The requirement of masks in public transport and secondary schools contributed to an overall 0.025 (CI. 0.018-0.030) reduction in Rt, compared to the baseline usage even when there are no mandates. Although causal relations are not guaranteed by the model framework, it nevertheless provides a fine-grained justification for the relative merits of choice and the degree of the NPIs and a data-driven strategy for mitigating Rt.

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Data ratcheting and data-driven organisational change in transport

Big Data & Society ◽

10.1177/2053951719867359 ◽

2019 ◽

Vol 6 (2) ◽

pp. 205395171986735 ◽

Cited By ~ 2

Author(s):

Liam Heaphy

Keyword(s):

Real Time ◽

Public Transport ◽

Traffic Control ◽

Organisational Change ◽

Digital Data ◽

Data Driven ◽

Intelligent Transport System ◽

Intelligent Transport ◽

Control Rooms ◽

Rational Order

This article explores the process by which intelligent transport system technologies have further advanced a data-driven culture in public transport and traffic control. Based on 12 interviews with transport engineers and fieldwork visits to three control rooms, it follows the implementation of Real-Time Passenger Information in Dublin and the various technologies on which it is dependent. It uses the concept of ‘data ratcheting’ to describe how a new data-driven rational order supplants a gradualist, conservative ethos, creating technological dependencies that pressure organisations to take control of their own data and curate accessibility to outside organisations. It is argued that the implementation of Real-Time Passenger Information forms part of a changing landscape of urban technologies as cities move from a phase of opening data silos and expanded communication across departments and with citizens towards one in which new streams of digital data are recognised for their value in stabilising novel forms of city administration.

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