Resilience Assessment of a Twin-Tube Motorway Tunnel in the Event of a Traffic Accident or Fire in a Tube

We have developed a traffic simulation model to quantitatively assess the resilience of a twin-tube motorway tunnel in the event of traffic accident or fire occurring within a tube. The motorway section containing the tunnel was investigated for different possible scenarios including its partial or complete closure. The functionality of the road infrastructure, in the case of an accident in one of the two tubes (each tube presents two lanes with unidirectional traffic under ordinary conditions), was assumed to be recovered both by using the remaining undisrupted lane of the tube interested by the disruptive event (only one lane is closed) and reorganizing the traffic flow by utilizing the adjacent tube for bi-directional traffic (both lanes are closed). The effects of an alternative itinerary individualized in the corresponding open road network were also examined. The level of functionality of the system during the period in which the tube is partially or completely closed was computed as the ratio between the average travel time required to reach a given destination from a specific origin before and after the occurrence of the disruptive event. The resilience metrics were assumed to be resilience loss, recovery speed, and resilience index. The best scenario was found to be the partial closure of the tube in contrast to the complete one. However, in order to contain the negative effects on the functionality of the motorway section due to the complete closure of the tube, it is worth highlighting how the traffic by-pass before the entrance portal of the closed tube should be open in a very short time by the tunnel management team to allow for the quick use of the adjacent tube for bi-directional traffic. An additional improvement, with reference exclusively to passenger cars traveling through the adjacent unblocked tube, might be obtained by activating the variable message signs, located at a sufficient distance from the motorway junction before the entrance portal of the closed tube, in order to suggest an alternative route to heavy good vehicles (HGVs) only. Whereas, when the alternative itinerary is used by all vehicles traveling towards the blocked tube (i.e., both passenger cars and HGVs), this redirectioning of the motorway traffic flow was found to be characterized by an excessive travel time, with it therefore not being advisable. The results obtained might be useful as a decision-making support tool aimed at improving the resilience of twin-tube tunnels.

Does Displaying Safety Messages on Dynamic Message Signs have Measurable Impacts on Crash Risk?

Transportation agencies have increasingly been using dynamic message signs (DMS) to communicate safety messages in an effort to both increase awareness of important safety issues and to influence driver behavior. Despite their widespread use, evaluations as to potential impacts on driver behavior, and the resultant impacts on traffic crashes, have been very limited. This study addresses this gap in the extant literature and assesses the relationship between traffic crashes and the frequency with which various types of safety messages are displayed. Safety message data were collected from a total of 202 DMS on freeways across the state of Michigan between 2014 and 2018. These data were integrated with traffic volume, roadway geometry, and crash data for segments that were located downstream of each DMS. A series of random parameters negative binomial models were estimated to examine total, speeding-related, and nighttime crashes based on historical messaging data while controlling for other site-specific factors. The results did not show any significant differences with respect to total crashes. Marginal declines in nighttime crashes were observed at locations with more frequent messages related to impaired driving, though these differences were also not statistically significant. Finally, speeding-related crashes were significantly less frequent near DMS that showed higher numbers of messages related to speeding or tailgating. Important issues are highlighted with respect to methodological concerns that arise in the analysis of such data. Field research is warranted to investigate potential impacts on driving behavior at the level of individual drivers.

Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads

Among the reasons for traffic accidents, distractions are the most common. Although there are many traffic signs on the road that contribute to safety, variable message signs (VMSs) require special attention, which is transformed into distraction. ADAS (advanced driver assistance system) devices are advanced systems that perceive the environment and provide assistance to the driver for his comfort or safety. This project aims to develop a prototype of a VMS (variable message sign) reading system using machine learning techniques, which are still not used, especially in this aspect. The assistant consists of two parts: a first one that recognizes the signal on the street and another one that extracts its text and transforms it into speech. For the first one, a set of images were labeled in PASCAL VOC format by manual annotations, scraping and data augmentation. With this dataset, the VMS recognition model was trained, a RetinaNet based off of ResNet50 pretrained on the dataset COCO. Firstly, in the reading process, the images were preprocessed and binarized to achieve the best possible quality. Finally, the extraction was done by the Tesseract OCR model in its 4.0 version, and the speech was done by the cloud service of IBM Watson Text to Speech.

Visual Perception and Understanding of Variable Message Signs: The Influence of the Drivers’ Age and Message Layout

Variable message signs (VMS) are used to display messages providing up-to-date traffic-relevant information so that drivers can safely adapt their behavior in real time. The information reported in a VMS should be brief but comprehensive to minimize perception time. The latter can be influenced by the way the message is displayed. This study investigates how the different ways of displaying the same message can influence reading time and the information perception process at different driving speeds. Specifically, the following message characteristics are investigated: (i) use of uppercase and lowercase letters; (ii) use of familiar pictograms; and (iii) use of less familiar pictograms. Furthermore, as perception time typically changes with ageing, drivers belonging to three different age classes are tested. The experimentation was performed by simulating a vehicle passing along a straight road upon which a VMS displaying different messages was placed. Experimentation results are analyzed using the Kruskal–Wallis test, Friedman rank-sum test and Welch one-way ANOVA, showing that: (i) the use of uppercase or lowercase does not seem to significantly affect reading times; (ii) the use of pictograms that are not very familiar to habitual road-users can be counterproductive for the perception process; (iii) elderly drivers always have greater difficulty in perceiving the message than young or middle-aged drivers. The findings of this study can be of help for traffic authorities to design the most suitable structure for a VMS so that its information can be unequivocally and immediately conveyed to drivers.

Examining Driver Compliance with a Move-Over/Slow Down Law in Consideration of Vehicle Type and Messages Displayed on Upstream Dynamic Message Signs

Move-over laws are intended to enhance the safety of road agency and law enforcement personnel who are working on or near the roadway. This study examined driver behavior through a series of field studies where these types of vehicles were located on the outside shoulder of a freeway with their lights activated. The study also evaluated the use of upstream dynamic message signs (DMS) to discern whether targeted safety messages had any impact on behavior under this scenario. Upstream and downstream speed and lane position data were collected from vehicles originally traveling in the rightmost lane upstream of the DMS and emergency/service vehicle at two locations in Michigan. Logistic regression models were estimated to assess driver compliance with the law while considering important contextual factors, such as the type of vehicle on the shoulder and the message displayed on the DMS. The results indicated that drivers were more likely to move over or reduce their speeds when a police car was located on the shoulder as compared to a transportation agency pickup truck. In general, the type of message displayed had minimal impact on driver behavior. The one exception showed that drivers were likely to drive at or below the speed limit when targeted move-over messages were shown as compared to standard travel time messages. For all message types, both speed and lane compliance improved if the roadside vehicle was a police car.