Real-time evaluation and management of extreme traffic load risk on main road’s bridges

The risk induced by extreme traffic loads on bridges was rarely investigated and the existing methods require computationally expensive elaborations that are not compatible with a real time risk management. Traditional approaches to reduce risk suggested the optimisation of bridge maintenance plans. Conversely, approaches that real-time evaluate and manage the risk are missing. Moreover, the integration of emerging prediction models, such as Artificial Neural Networks, was never explored. This study fills the previous gaps by proposing a three-block methodology. It adopts Weight-In-Motion systems to collect site-traffic load data, formulates a probabilistic Risk Prediction Model to estimate frequency and severity of bridge failure events according to Eurocodes, and simulates an Intelligent Transportation System (ITS) architecture to apply real time management actions. The methodology was tested on 2.5M+ vehicles raw WIM data gathered along the ring road of Brescia (Italy). Bridge failure events resulted significantly more frequent than that prescribed by Eurocode, and factors of compliance with Traffic Code mass limits prescriptions had the more significant effect on risk predictions. The findings suggest a greater attention when permits for extremely overweighed vehicles are issued, as well as the implementation of enforcement strategies and ITS-based architectures for the real time risk management

Urban transport management and customer perceived quality: a case study in the metropolitan area of Cagliari, Italy

Liberalization processes and the threats posed by ever increasing competition have forced public transport companies in Italy to pay a closer attention to their customers, so to enhance loyalty and demand for transport. These customer-targeted strategies have been responsible for some significant changes of otherwise consolidated practices, in an attempt to lure new transit riders and discourage potential competitors from entering local markets. The present study describes the results of an on-board survey implemented within the Cagliari’s metropolitan area and aimed at discovering the degree of perceived quality toward the offer of public transport managed by CTM S.p.A, the local transport agency in charge for the planning and management of public mobility around the Sardinian’ regional capital. The research focuses on the implementation of an Impact Score methodology which, following the suggestions included in the Transit Cooperative Research Program Report n°47 (1999), assesses the degree of perceived quality with reference to ten core attributes of the public transport. The survey results show a substantial lack of structural problems and a high level of perceived quality, although some attributes need to be addressed if CTM aims to preserve and strengthen the central role so far enjoyed

Evaluating bus accident risks in public transport

Public transit buses may be considered a safer transportation mode as opposed to others (e.g., private cars). However, safety is a crucial issue regarding transit buses from the perspectives of operators and passengers due to the relevant implications it generates. Therefore, evaluating the accident risk on bus routes provides an opportunity to improve the safety performance of transit operators. Previous research identified patterns of bus accidents and shed light on understanding the effects of many factors regarding frequency and severity of bus accidents. However, no studies have investigated accident risks in bus transit, while considering frequency, severity and exposure factors in a single function. This paper proposes a new methodology for evaluating the accident risk for each transit bus route. At first, the methodology identifies the risk components in terms of frequency, severity and exposure factors that may affect bus accidents. Next, it integrates these terms, to build a risk bus accident function providing a ranking of safety performance for each route. The feasibility of this methodology is demonstrated in a real case study using 3,457 bus accidents provided by a mid-sized Italian bus operator. This experiment shows that transit managers could adopt this methodology to perform an accurate safety analysis on each route. Moreover, this methodology may be implemented in a road traffic safety management system for bus transit operators interested in the monitoring of safety performance, in the evaluation of the risk of accidents on routes, and in the certification process according to recent safety norms

Standing Passenger Comfort: A New Scale for Evaluating the Real-Time Driving Style of Bus Transit Services

On-board bus comfort is a key factor affecting the quality of transit service. Thus, its assessment is crucial for public transport companies, as it can support the monitoring, evaluation and implementation of specific actions to improve their services. Previous research mainly focused on separate subjective and objective measurements of on-board comfort. Furthermore, even if concurrent measurements of objective and subjective on-board comfort have been collected, no study has built a gradual scale for the real-time measurement of comfort. This paper covers this gap by integrating subjective measurements of driving style with objective measurements of longitudinal and transversal accelerations collected by intelligent transportation system tools. These findings are very useful because they represent the first contribution for establishing a comfort scale in a real operational environment as a tool to regulate driver behavior, i.e., each driver will be able to recognize when passengers experience conditions of discomfort and acts to improve comfort

iABACUS: A Wi-Fi-Based Automatic Bus Passenger Counting System

Since the early stages of the Internet-of-Things (IoT), one of the application scenarios that have been affected the most by this new paradigm is mobility. Smart Cities have greatly benefited from the awareness of some people’s habits to develop efficient mobility services. In particular, knowing how people use public transportation services and move throughout urban infrastructure is crucial in several areas, among which the most prominent are tourism and transportation. Indeed, especially for Public Transportation Companies (PTCs), long- and short-term planning of the transit network requires having a thorough knowledge of the flows of passengers in and out vehicles. Thanks to the ubiquitous presence of Internet connections, this knowledge can be easily enabled by sensors deployed on board of public transport vehicles. In this paper, a Wi-Fi-based Automatic Bus pAssenger CoUnting System, named iABACUS, is presented. The objective of iABACUS is to observe and analyze urban mobility by tracking passengers throughout their journey on public transportation vehicles, without the need for them to take any action. Test results proves that iABACUS efficiently detects the number of devices with an active Wi-Fi interface, with an accuracy of 100% in the static case and almost 94% in the dynamic case. In the latter case, there is a random error that only appears when two bus stops are very close to each other

Assessing the Risk of Bus Crashes in Transit Systems

Although public transport buses may be considered a safe transportation mode, bus safety is a crucial issue from the perspectives of operators, passengers and local authorities owing to the relevant implications it generates. Therefore, assessing the risk of crashes on bus routes may help improve the safety performance of transit operators. Much research has identified patterns of bus crashes to understand the effects of many factors on the frequency and the severity of them. Conversely, to the best of our knowledge, the research measuring the risk of crashes in bus transit networks is seldom faced. This paper adjusts existing methods to assess the safety on bus transit networks by the integration of safety factors, prediction models and risk methods. More recisely, first, the methodology identifies several safety factors as well as the exposure risk factors. Second, this methodology specifies the risk components in terms of frequency, severity and exposure factors that may affect bus crashes and models their relationships in a risk function. Third, this methodology computes the risk of crashes for each route and provides a ranking of safety performance. A real case study demonstrates the feasibility of this methodology using 3,457 bus crashes provided by a mid-sized Italian bus operator. This experiment shows that transit managers could adopt this methodology to perform an accurate safety analysis on each route. Moreover, this methodology could be implemented in a road traffic safety management system in order to evaluate the risk of crashes on routes, monitor the safety performance of each route and qualify each route according to recent safety norms