Safety assurance for automated driving systems that can adapt using machine learning: A qualitative interview study.

Introduction: Automated Driving Systems (ADSs) present significant unresolved challenges for traditional safety assurance frameworks. These frameworks did not envisage, nor readily support, automated driving without the active involvement of a human driver, or support safety-critical systems using Machine Learning (ML) to modify their driving functionality during in-service operation.

Method: An in-depth qualitative interview study was conducted as part of a broader research project on safety assurance of ADSs that can adapt using ML.

Pedestrian Origin-Destination Estimation Based on Multi-Camera Person Re-Identification.

Pedestrian origin-destination (O-D) estimates that record traffic flows between origins and destinations, are essential for the management of pedestrian facilities including pedestrian flow simulation in the planning phase and crowd control in the operation phase. However, current O-D data collection techniques such as surveys, mobile sensing using GPS, Wi-Fi, and Bluetooth, and smart card data have the disadvantage that they are either time consuming and costly, or cannot provide complete O-D information for pedestrian facilities without entrances and exits or pedestrian flow inside the facilities. Due to the full coverage of CCTV cameras and the huge potential of image processing techniques, we address the challenges of pedestrian O-D estimation and propose an image-based O-D estimation framework.

Pedestrian flow characteristics through different angled bends: Exploring the spatial variation of velocity.

Common geometrical layouts could potentially be bottlenecks, particularly during emergency and high density situations. When pedestrians are interacting with such complex geometrical settings, the congestion effect might not be uniform over the bottleneck area. This study uses the trajectory data collected through a controlled laboratory experiment to explore the spatial variation of speeds when a group of people navigates through bends.

An outer approximation method for the road network design problem.

Best investment in the road infrastructure or the network design is perceived as a fundamental and benchmark problem in transportation. Given a set of candidate road projects with associated costs, finding the best subset with respect to a limited budget is known as a bilevel Discrete Network Design Problem (DNDP) of NP-hard computationally complexity. We engage with the complexity with a hybrid exact-heuristic methodology based on a two-stage relaxation as follows: (i) the bilevel feature is relaxed to a single-level problem by taking the network performance function of the upper level into the user equilibrium traffic assignment problem (UE-TAP) in the lower level as a constraint.

Collective movements of pedestrians: How we can learn from simple experiments with non-human (ant) crowds.

Introduction: Understanding collective behavior of moving organisms and how interactions between individuals govern their collective motion has triggered a growing number of studies. Similarities have been observed between the scale-free behavioral aspects of various systems (i.e.

A Doppler Radar System for Sensing Physiological Parameters in Walking and Standing Positions.

Doppler radar can be implemented for sensing physiological parameters wirelessly at a distance. Detecting respiration rate, an important human body parameter, is essential in a range of applications like emergency and military healthcare environments, and Doppler radar records actual chest motion. One challenge in using Doppler radar is being able to monitor several patients simultaneously and in different situations like standing, walking, or lying.

How Simple Hypothetical-Choice Experiments Can Be Utilized to Learn Humans' Navigational Escape Decisions in Emergencies.

How humans resolve non-trivial tradeoffs in their navigational choices between the social interactions (e.g., the presence and movements of others) and the physical factors (e.

Macroscopic modeling of pedestrian and bicycle crashes: A cross-comparison of estimation methods.

The paper presents a cross-comparison of different estimation methods to model pedestrian and bicycle crashes. The study contributes to macro level safety studies by providing further methodological and empirical evidence on the various factors that influence the frequency of pedestrian and bicycle crashes at the planning level. Random parameter negative binomial (RPNB) models are estimated to explore the effects of various planning factors associated with total, serious injury and minor injury crashes while accounting for unobserved heterogeneity.

Application of a random effects negative binomial model to examine tram-involved crash frequency on route sections in Melbourne, Australia.

Safety is a key concern in the design, operation and development of light rail systems including trams or streetcars as they impose crash risks on road users in terms of crash frequency and severity. The aim of this study is to identify key traffic, transit and route factors that influence tram-involved crash frequencies along tram route sections in Melbourne. A random effects negative binomial (RENB) regression model was developed to analyze crash frequency data obtained from Yarra Trams, the tram operator in Melbourne.

Safety impacts of platform tram stops on pedestrians in mixed traffic operation: A comparison group before-after crash study.

Tram stops in mixed traffic environments present a variety of safety, accessibility and transport efficiency challenges. In Melbourne, Australia the hundred year-old electric tram system is progressively being modernized to improve passenger accessibility. Platform stops, incorporating raised platforms for level entry into low floor trams, are being retro-fitted system-wide to replace older design stops.

An empirical Bayes safety evaluation of tram/streetcar signal and lane priority measures in Melbourne.

Objective: Streetcars/tram systems are growing worldwide, and many are given priority to increase speed and reliability performance in mixed traffic conditions. Research related to the road safety impact of tram priority is limited. This study explores the road safety impacts of tram priority measures including lane and intersection/signal priority measures.

Simulation of safety: a review of the state of the art in road safety simulation modelling.

Recent decades have seen considerable growth in computer capabilities, data collection technology and communication mediums. This growth has had considerable impact on our ability to replicate driver behaviour and understand the processes involved in failures in the traffic system. From time to time it is necessary to assess the level of development as a basis of determining how far we have come.

Factors affecting the probability of bus drivers being at-fault in bus-involved accidents.

Previous research has provided little insight into factors that influence the probability of bus drivers being at-fault in bus-involved accidents. In this study, an analysis was conducted on accident data compiled by a bus company that include an assessment on whether the bus driver was deemed by the company to hold primary responsibility for accident occurrence. Using a mixed logit modelling approach, roadway/environmental, vehicle and driver related variables that were identified to be influential were road type, speed limit, traffic/lighting conditions, bus priority, bus age/length and driver's age/gender/experience/historic at-fault accident record.

Bus accident analysis of routes with/without bus priority.

This paper summarises findings on road safety performance and bus-involved accidents in Melbourne along roads where bus priority measures had been applied. Results from an empirical analysis of the accident types revealed significant reduction in the proportion of accidents involving buses hitting stationary objects and vehicles, which suggests the effect of bus priority in addressing manoeuvrability issues for buses. A mixed-effects negative binomial (MENB) regression and back-propagation neural network (BPNN) modelling of bus accidents considering wider influences on accident rates at a route section level also revealed significant safety benefits when bus priority is provided.