Ths session covers non-punative employee safety systems, use of photo and video documentation, the impact of using mobile phones while driving on attitudes toward speeding, and an analysis of eye witness accounts and passenger tweets.
Characteristics of Non-Punitive Employee Safety Reporting Systems for Public Transportation: Abridged Version of TCRP Report 218
Lisa Staes, USF Center for Urban Transportation ResearchShow Abstract
Jodi Godfrey, University of South Florida
The objective of this research is to produce a compilation of best practices used in non-punitive Employee Safety Reporting (ESR) systems at transit agencies, including examples of how ESR systems benefit transit agencies and their employees. This report will support the public transportation industry’s efforts to institute non-punitive ESR as a critical element in Safety Management Systems (SMS) implementation. The literature review and background research framed the subsequent narrative and findings from interviews with public transportation agencies. For the purposes of this report, the agency that implemented the ESR system determined successful elements. The researchers did not perform a statistical modeling or evaluation method to determine elemental success, rather success determination stemmed from the implementing agencies. This report also identifies challenges faced through the implementation phases of ESR system deployment, as presented through the literature review and transit agency case studies. Report findings identify benefits associated with wide dissemination of commonly reported hazards and methods to address them, such as the Aviation Safety Reporting System, or the Confidential Close-Call Reporting System. There are also recognized benefits in third party administration and management of ESR systems through reduced likelihoods of associated punitive or retaliatory consequences. Therefore, researchers determined the public transportation industry could benefit from central repository reporting options for hazards and near-miss information aggregation to further support data-driven decision-making. Additionally, industry evidentiary protections would ensure greater reporting. Finally, the public transportation industry would benefit from a non-punitive ESR toolkit or online resource repository that includes samples for agency customization.
Mainstreaming Photo and Video-based Documentation as Method for Establishing a LOS Framework for the Mumbai Suburban Railway System
Leona Nunes, World Resources InstituteShow Abstract
Lubaina Rangwala, World Resources Institute
Madhav Pai, World Resources Institute
The city of Mumbai has grown at an unprecedented rate, increasing the burden of mobility on its core public transport system, the Mumbai suburban railway network. It is likely that the system is failing due to ‘over optimization’, and the fact that stations aren’t designed to cater to needs of a rapidly growing city, has led to a steady surge in fatalities over the years, primarily in the metropolitan region beyond city limits. Besides fatalities, recent research shows, that crowding has led to extreme fear and insecurity, especially in women and young commuters vulnerable to petty thefts and sexual abuse. There is a critical need to decongest the Mumbai suburban rail network across the system: including station areas, platforms, inside coaches and on access infrastructure like pedestrian ramps, bridges and stairways. Concepts such as, level of service (LOS), overcrowding and crowd management can be used to address this need, however, there are two critical challenges. One, these concepts, largely developed in global North cities, are inadequate in dealing with the kinds of commuter densities and complex station area economies, typical to cities like Mumbai. Two, conventional data gathering methods are time consuming, costly, and inflexible in capturing dynamic commuter behavior, critical to the science of crowd management. This paper aims to address these two challenges by, articulating a set of ‘probes’ that can inform a localized framework towards effective crowd management on the Mumbai local trains, and proposing dynamic data capture methods that inform and enable a scientific planning process.
What do Riders Say and Where? The Detection and Analysis of Eyewitness Transit Tweets
Omar Kabbani, University of TorontoShow Abstract
Willem Klumpenhouwer, University of Toronto
Amer Shalaby, University of Toronto
Tamer El-Diraby, University of Toronto
Analyzing Twitter data is useful for planning and operating transportation systems. Twitter provides an unfiltered and timestamped feed of information that can be aggregated to generate valuable insights. Our research creates a framework for processing a public Twitter feed to identify passenger–related transit incidents. Detecting these incidents in real time enables transit agencies to immediately respond to them by dispatching security, safety, or maintenance crews, and in the context of the current COVID–19 pandemic, to provide targeted cleaning measures to combat the spread of the virus. Using natural language processing, we identify eyewitness tweets about transit and then extract latent information from the tweets such as location details, sentiments, and topics. This enables agencies to respond to an incident faster and to identify spatial and temporal patterns for incidents and interests throughout the network.
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