Suburban Roads Research Articles

Intertwining Community-Driven and Student-Built Approaches to Activate Suburban Streets

Suburban streets often lack vitality and fail to foster social interaction and community cohesion. Traditional approaches to retrofitting tend to focus on static spatial interventions, proving costly and inadequate for addressing dynamic community needs. This paper explores flexible, cost-effective solutions that can activate suburban streets and enhance cultural cohesion. Through a brief review of street retrofitting concepts and practices, the authors investigate ways in which an inclusive approach can promote community engagement and transform streets into dynamic social spaces. This approach consists of a community-driven and student-built collaborative design, prefabrication and installation, and adaptive use by the community. Examining Tāmaki Makaurau Auckland in Aotearoa New Zealand as a context rich in cultural diversity and with a demand for vibrant public spaces, the authors tested this new approach through design interventions by Unitec architecture students in Open Streets events in Avondale, Tāmaki Makaurau Auckland. The authors then tested this approach with two projects: Toutai-'a Maui: Maui’s Catch at the Whau Arts Festival, and the Woven Gateway at the We Are Woven Festival. The positive outcomes of these two projects demonstrate the potential of temporary architectural interventions in activating streets and fostering conversation about the streets of Avondale.

DEPOSITION OF PRIORITY POLLUTANTS FROM MOTOR VEHICLE EMISSIONS BY SNOW COVER IN THE ROADSIDE SPACE

The microzone of chemical pollution is crucial for the level of environmental safety of road operation and their interaction with the environment – the transfer of pollutants generated by the combustion of fuels in engines and substances used in road maintenance by air and water. The chemical composition of exhaust gases entering the lower atmosphere in the human breathing zone is dangerous not only for human health but also for the formation of greenhouse gases. The environmental hazard of nitrogen compound emissions is caused by the formation of fine particulate matter (PM2.5) in the atmosphere and nitrogen deposition in ecosystems. Roadside snow cover is an extremely informative substrate for monitoring the level and characteristics of roadside pollution by motor vehicle emissions. It adsorbs emission components, which are identified in melted snow as nitrogen compounds – nitrates and ammonium ion, particles in the form of suspended solids, hydrocarbons in the form of petroleum products. These contaminants form an environmental hazard of surface wastewater generated on the roadside for soils and natural water bodies. The aim of the study is to experimentally determine the contamination of snow in the roadside area of urban and suburban roads by the main environmentally hazardous pollutants of exhaust gases immobilized in the snow cover: particles (suspended solids), hydrocarbons (petroleum products), and nitrogen compounds. An experimental study of the contamination of the snow cover in the roadside area with priority pollutants (ammonium, nitrates, suspended particles, petroleum products) from vehicle emissions was carried out. It was found that in the process of exposure of the snow cover, the concentration of all studied pollutants in it increased steadily compared to the content in atmospheric snow. With increasing distance from urban and suburban roads, snow contamination with suspended solids and petroleum products was negatively correlated. This demonstrates the level of anthropogenic load created by motor vehicle traffic in the studied areas. The concentration of nitrogen-containing compounds – ammonium nitrogen and nitrates – had a positive correlation and increased with increasing distance from the road. Thus, on the studied road sections, there was a simultaneous emission of ammonium nitrogen and nitrogen oxides, which creates a particularly dangerous environmental situation in urban areas due to the formation of PM2.5.

Assessing the risk of pedestrian crossing behavior on suburban roads using structural equation model

While pedestrian crashes on suburban roads have received more attention over recent years, the role of pedestrian crossing risk in areas adjacent to pedestrian crossing facilities, such as pedestrian overpasses, has been neglected. Most pedestrians in suburban areas tend to avoid pedestrian overpasses, exhibiting crossing behaviors that increase the likelihood of pedestrian-involving crashes. As a result of the presence of overpasses, drivers may think that there are no pedestrians in the surroundings, so they choose a speed based only on the prevailing traffic and road environment without accounting for potential interactions with pedestrians. Consequently, crashes will occur, with pedestrians typically being the most seriously affected casualties. In this study, using video recordings from a suburban road in Amol-Babol, Iran, the risk of pedestrian crossing behavior in areas near pedestrian overpasses is investigated. The speed selection behavior of drivers in these areas has also been examined using speedometer cameras. To quantify the level of risk for pedestrians when interacting with approaching vehicles during the crossing movements, the post encroachment time (PET) was used as a surrogate safety measure. Based on critical thresholds of PET, three different risk levels were identified using a K-means algorithm: high, medium, and low risk. To identify the elements affecting the risk of pedestrian crossing behavior, structural equation models were estimated for all three risk levels. The results showed that human factors, relating to both drivers and pedestrians, have a dominant impact on pedestrian safety, especially in high and medium risk contexts. Road and vehicle factors were also found to have statistically observable effects on pedestrian safety, but to a milder extent compared to human factors. The findings of this study highlight the need for intervening in several aspects of vehicle-pedestrian interactions with critical importance for pedestrian safety, including road users’ performance and compliance, state of alertness, and interaction with road infrastructure.

A real-time prediction method for PEMFC life under actual operating conditions

The life prediction of proton exchange membrane fuel cell (PEMFC) is one of the methods to reduce the cost of using fuel cells. This helps to solve the problem of high using costs for fuel cell vehicles, which makes it difficult to commercialize them on a large scale. However, there is still a lack of real-time life prediction methods that can be applied to fuel cell vehicles. In this article, the first step is conducting actual road vehicle life experiments, which include urban roads, suburban roads, and highways. Collect experimental data and preprocess it to obtain a dataset of actual vehicle life. The second step is to divide the output power of the fuel cell into multiple operating conditions, calculate the voltage decay rate of each operating condition, and establish a real-time prediction (RTP) model for the fuel cell life. The third step is conducting fuel cell life prediction. The results show that compared with the Pei method, the RMSE and MAPE of the proposed RTP method for fuel cell life have decreased by 21% and 14% in the long-term prediction of 100 h, and the average reduction is 41% and 35% in the short-term prediction of 10 h.

Leveraging SUMO for Real-World Traffic Optimization: A Comprehensive Approach

This paper illuminates the utilization of SUMO as a powerful tool for addressing real-world traffic management issues. There is a gap in testing and validating solutions to in-field conditions due to the high cost and complexity of urban and suburban road networks. The validation step is often skipped, which can lead to a higher risk in implementing sophisticated solutions that exist in our multimodal transportation environment. This challenge is addressed by introducing simulations as a crucial preliminary step before real-world application. Accurate simulations require detailed data on intersection geometries, vehicle distribution, and driver behavior to accurately mirror real-world conditions. To meet these criteria, detailed sensor data on trajectories, types of road users, and their locations are extensively employed. This data forms the foundation for calibrated traffic simulations by NoTraffic™ . In conclusion, an in-depth demonstration of the method used to address a real-world traffic problem with SUMO is provided, emphasizing SUMO’s effectiveness in building confidence for deploying solutions in the field.

Spatial Mapping of On-road Traffic Emission for Air Quality Management: A Case of Vinh Phuc Province

Vinh Phuc province is in the northern key economic region of Vietnam. The province has been facing challenges in air quality management as the number of vehicles has rapidly increased to meet the rising demand for transportation resources. This research was aimed to conduct an emission inventory and to build a spatial emission map for traffic activities in an attempt to improve air quality management in Vinh Phuc province. The vehicles were categorized into 5 groups: motorcycles, cars, light-duty vehicles (LDVs), heavy-duty vehicles (HDVs) and buses. Meanwhile, the streets were also categorized into 5 groups: highways, rural roads, urban streets, suburban streets and industrial streets. The results showed that motorcycles were the main means of transportation (93% of total vehicles) and they were also the major contributors to total emissions of NOx, CO, VOCs (volatile organic compounds), CH4, NMVOCs (non-methane volatile organic compounds) and especially in the cases of CO, CH4, VOC and NMVOC emissions which contributed more than 90% of emissions. Cars were the main source of SO2 emissions, contributing 51% of total SO2 road traffic emissions. The emissions of TSP (total suspended particulate), PM10 and PM2.5 were mostly generated from buses (about 32%), followed by motorcycles (about 18%). LDVs and HDVs contributed 18% and 15% to total particulate matter emissions, respectively. Spatial distribution analysis of CO, NOx, SO2, TSP, NMVOC and PM2.5 which involved visual identification in polluted areas, showing that high emissions were in the southeast part of the province and the most polluted areas were Vinh Yen city, followed by Binh Xuyen district and Phuc Yen city. These results provide suggestions for local governments on how to design effective air quality control strategies.

Does using a bioptic telescope improve visual recognition of the on-road environment?

Individuals with reduced visual acuity (VA) may drive with bioptic telescopes in some jurisdictions. The effect of bioptic telescope use on on-road recognition distances is important for driving safety, as increased recognition distances increase the time available to react to road signs or driving events. This study aimed to investigate the impact of bioptic telescope use on visual recognition of road signs, traffic lights, and hazards in an on-road driving environment in individuals with reduced VA. Ten individuals (mean ± standard deviation age, 39.1 ± 19.1 years) with reduced VA (trained to use bioptic telescopes) participated in a repeated-measures on-road experiment for two viewing conditions, with and without a bioptic telescope (randomized order). Participants underwent a clinical low vision assessment, including high-contrast VA with and without the bioptic telescope aligned, visual fields, and contrast sensitivity testing. For the driving component, participants seated in the front passenger seat of a moving car reported all road signs, traffic lights, and hazards (unpredictable road events that represent a risk to driving safety including other vehicles, cyclists, pedestrians) seen along a route that included suburban roads and highways. Video cameras captured participants' viewing behavior, verbal commentary, and the distance at which three pre-selected road signs were reported. In the eye with the bioptic telescope, high-contrast VA improved from 0.75 ± 0.17 (without) to 0.25 ± 0.1 logMAR with the bioptic telescope. Bioptic telescope use did not affect the percentage of road signs, traffic lights, or hazards correctly recognized but did result in 2.6 times longer recognition distances (49 ± 23 vs. 19 ± 11 m, t9 = 5.02, p<0.001). Road signs were recognized at significantly longer distances when using a bioptic telescope, confirming their positive impact on timely visual recognition of objects within the driving scene. Future work should explore whether this effect generalizes to individuals using bioptic telescopes when driving a vehicle.

On-road evaluation and regulatory recommendations for NOx and particle number emissions of China VI heavy-duty diesel trucks: A case study in Shenzhen

This research analyzed the real-world NOx and particle number (PN) emissions of 21 China VI heavy-duty diesel trucks (HDDTs). On-road emission conformity was first evaluated with portable emission measurement system (PEMS). Only 76.19 %, 71.43 % and 61.90 % of the vehicles passed the NOx test, PN test and both tests, respectively. The impacts of vehicle features including exhaust gas recirculation (EGR) equipment, mileage and tractive tonnage were then assessed. Results demonstrated that EGR helped reducing NOx emission factors (EFs) while increased PN EFs. Larger mileages and tractive tonnages corresponded to higher NOx and PN EFs, respectively. In-depth analyses regarding the influences of operating conditions on emissions were conducted with both numerical comparisons and statistical tests. Results proved that HDDTs generated higher NOx EFs under low speeds or large vehicle specific powers (VSPs), and higher PN EFs under high speeds or small VSPs in general. In addition, unqualified vehicles generated significantly higher NOx EFs than qualified vehicles on freeways or under speed≥40 km/h, while significant higher PN EFs were generated on suburban roads, freeways or under operating modes with positive VSPs by unqualified vehicles. The reliability and accuracy of on-board diagnostic (OBD) NOx data were finally investigated. Results revealed that 43 % of the test vehicles did not report reliable OBD data. Correlation analyses between OBD NOx and PEMS measurements further demonstrated that the consistency of instantaneous concentrations were generally low. However, sliding window averaged concentrations show better correlations, e.g., the Pearson correlation coefficients on 20s-window averaged concentrations exceeded 0.85 for most vehicles. The research results provide valuable insights into emission regulation, e.g., focusing more on medium- to high-speed operations to identify unqualified vehicles, setting higher standards to improve the quality of OBD data, and adopting window averaged OBD NOx concentrations in evaluating vehicle emission performance.

Safety Assessment of Suburban-Type Arterial Roadways: New Findings Using Heterogeneity Models

This study investigates the safety performance of suburban-type roads (STRs), a category of medium-speed arterial roads characterized by high vehicular traffic and limited pedestrian and cyclist mobility. Such roadways present unique challenges in road safety, often overlooked in traditional road design focused predominantly on driver needs. By employing a correlated random parameter ordered probit model to analyze police-reported crash severity data from STRs, this research uncovers critical insights into the factors influencing crash injury severities on these roads. Our findings highlight the nuanced impact of various dimensions, such as road design, traffic volume, and environmental conditions, on STR safety; the results offer a detailed understanding of crash contexts on STRs and point toward targeted interventions for enhancing road safety. These interventions include the potential redesign of roadways to accommodate diverse user needs and the implementation of speed regulation measures tailored to specific road characteristics. This research contributes significantly to the existing body of knowledge on suburban road safety, providing evidence-based recommendations for improving the overall safety of these increasingly prevalent road types. The implications of these findings are far-reaching, offering a foundation for future studies to explore diverse methodologies in road safety analysis and to extend this research to other geographic contexts.

Development of Capacity Estimation Models for Multi-lane Roads under Heterogeneous Traffic Conditions

The objective of this study is to develop capacity models to estimate the traffic carrying capacity of multi-lane roads with heterogeneous traffic flows. Since capacity is an important parameter in traffic engineering studies, it is vital that it is accurately estimated. Most of the current capacity estimation methodologies available are developed based on homogeneous traffic flows and hence not conducive for use in heterogeneous traffic conditions observed in Sri Lanka. Therefore, this study is done to fulfill the requirement of a capacity estimation model for this purpose. Fifty midblock sections were surveyed using manual flow data collection methods and a novel Google Application Program Interface (API) based method for speed data collection. Based on the collected data, capacity values were developed following the fundamentals of traffic flow. Regression models were built to estimate four-lane and six-lane capacity values based on roadway characteristics such as effective lane width, access point density, median type, and built environment condition. Separate models were developed because the impact these characteristics have on four-lane and six-lane roads are different. The models showed good fit with R2 values of 0.81 and 0.86 for four-lane and six-lane roads, respectively. The base capacity value of a four-lane urban road was estimated to be 2044 pcu/h/l and for a six-lane sub-urban road section 2108 pcu/h/l. The outcomes of this study can be used to develop capacity guidelines in countries with heterogeneous traffic conditions for capacity estimation.

Dimensions management of traffic big data for short-term traffic prediction on suburban roadways

Since intelligent systems were developed to collect traffic data, this data can be collected at high volume, velocity, and variety, resulting in big traffic data. In previous studies, dealing with the large volume of big traffic data has always been discussed. In this study, big traffic data were used to predict traffic state on a section of suburban road from Karaj to Chalous located in the north of Iran. Due to the many and various extracted features, data dimensions management is necessary. This management was accomplished using principal component analysis to reduce the number of features, genetic algorithms to select features influencing traffic states, and cyclic features to change the nature of features. The data set obtained from each method is used as input to the models. The models used include long short-term memory, support vector machine, and random forest. The results show that using cyclic features can increase traffic state prediction's accuracy than the model, including all the initial features (base model). Long short-term memory model with 71 cyclic features offers the highest accuracy, equivalent to 88.09%. Additionally, this model's reduced number of features led to a shorter modelling execution time, from 456 s (base model) to 201 s.

Energy Efficiency Analysis and Research of Pure Electric Vehicles under Actual Working Conditions

Pure electric vehicles are a hot topic in today’s social development. From beginning to end, the energy density of power batteries in pure electric vehicles is generally low, resulting in short range, inaccurate range estimation, and driver anxiety. This is also one of the reasons that hinder the promotion of pure electric vehicles. This article discusses the energy consumption of pure electric vehicles based on actual road tests. By comparing the battery energy efficiency of various roads (including urban roads, suburban roads, and highways) using electric vehicles from Guangzhou to Qingyuan, the energy consumption patterns of pure electric vehicles are analyzed. Experiments have shown that continuously improving the energy density of power batteries is the key technology to solve the problem of insufficient range for electric vehicles. Based on the existing power battery technology, this study investigates the relationship between energy consumption and various factors affecting electric vehicles, which can provide better reference for drivers to predict energy consumption and range.

Design and Implementation of a Real-Time Street Light Dimming System Based on a Hybrid Control Architecture

Smart street LED lighting systems have received much attention driven by the need to save energy and the dramatic advances in the Internet of Things. This work proposes a new smart street lighting system that adaptively changes the street lights’ intensity based on traffic and weather conditions and provides a platform for monitoring road conditions and detecting lamp faults. The system transfers the data using the UDP protocol over NBIoT radio technology. It also maintains two-way communication between the luminaires and the central node. In order to ensure real-time response to traffic and avoid dimming delays, each light is locally controlled by a microcontroller based on the sensed traffic and weather data. The measurements of each luminaire are also sent to the central control node to locate lamp faults, detect emergency situations, and, if needed, broadcast on/off messages to the whole network’s luminaires. The system was implemented in a suburban street in Ras Al Khaimah. Evaluations proved that the system can locate and detect faulty lamps and vary the light intensity in real time based on traffic. It also resulted in energy savings of up to 55% compared to a normal LED street light network.

Does in-vehicle automation help individuals with Parkinson's disease? A preliminary analysis.

PD is a progressive neurodegenerative disorder that affects, according to the ICF, body systems (cognitive, visual, and motor), and functions (e.g., decreased executive functions, decreased visual acuity, impaired contrast sensitivity, decreased coordination)-all which impact driving performance, an instrumental activity of daily living in the domain of "Activity" and "Participation" according to the ICF. Although there is strong evidence of impaired driving performance in PD, few studies have explored the real-world benefits of in-vehicle automation technologies, such as in-vehicle information systems (IVIS) and advanced driver assistance systems (ADAS), for drivers with PD. These technologies hold potential to alleviate driving impairments, reduce errors, and improve overall performance, allowing individuals with PD to maintain their mobility and independence more safely and for longer periods. This preliminary study aimed to fill the gap in the literature by examining the impact of IVIS and ADAS on driving safety, as indicated by the number of driving errors made by people with PD in an on-road study. Forty-five adults with diagnosed PD drove a 2019 Toyota Camry equipped with IVIS and ADAS features (Toyota Safety Sense 2.0) on a route containing highway and suburban roads. Participants drove half of the route with the IVIS and ADAS systems activated and the other half with the systems deactivated. The results suggest that systems that assume control of the driving task, such as adaptive cruise control, were most effective in reducing driving errors. Furthermore, individual differences in cognitive abilities, particularly memory, were significantly correlated with the total number of driving errors when the systems were deactivated, but no significant correlations were present when the systems were activated. Physical capability factors, such as rigidity and bradykinesia, were not significantly correlated with driving error. Taken together, these results show that in-vehicle driver automation systems can benefit drivers with PD and diminish the impact of individual differences in driver cognitive ability.

HISTORY OF FORMATION AND DEVELOPMENT THE UKRAINIAN SCIENCE CITY OF PYATKHATKI. THE ARCHITECTURAL, PLANNING AND FUNCTIONAL SPECIFICS

The article investigates the architectural and urban planning specifics of the center of innovation activity - Pyatikhatka in Kharkiv. This urban formation – "science city" – is considered as an industrial and urban planning phenomenon. The article highlights the history of the Ukrainian Institute of Physics and Technology and the main stages of creation of the science city on its basis. It is established that the Pyatikhatky Science City was built according to an individual master plan developed in the 1950s and early 1980s. The author identifies 2 main periods of construction, development and transformation of the science city: the soviet period and the period of independence, which in turn can be divided into stages. The Soviet period – the period of construction and development – is divided into two stages: 1950s - 1970s and 1980s - 1991, the period of independence – 1991-present - is the stage from 1992 – 2021 and the stage after February 24, 2022. It has been established that the settlement of Pyatikhatky had all the signs of a science city. The new urban formation was integrated into the network of suburban road connections, but the main research and production complex demonstrated the "transport dead end" characteristic of such formations (impossibility of passing through it). The territory was separated from the city by a green strip of forest and parkland, i.e. it had a sign of tightness/isolation from the surrounding urban areas. There was also an educational function, which was usually organically included in the structure of all science towns. The village had a developed sphere of services and culture, which were especially active in the soviet years, and an atypical planning structure with a classified master plan. The urban planning and architectural solutions of the entity and the improved urban environment demonstrated the latest progressive approaches of the time. It is concluded that for the successful recovery and development of our country in the current conditions, it is necessary to introduce a continuous cycle: education – science – entrepreneurship - production, which is why the revitalization and development of such a center of innovation activity has significant potential and great importance for the development of Ukraine. Keywords: innovation center, Kharkiv, Pyatikhatki, science city, architectural and urban planning features.

Estimation of psychoacoustic and noise indices from the sound pressure level of transportation noise sources: Investigation of their potential benefit to the prediction of long-term noise annoyance

Few research works tackled the testing of noise annoyance models proposed in the literature by considering a new set of survey data. This lack contributes to limit the effect of endeavors in noise annoyance prediction. This lack is observed for both annoyance models built from field data and laboratory data. Different reasons might explain this. In the case of annoyance models based on psychoacoustic indices accounting for annoying auditory sensations, their relevance was highlighted in laboratory conditions as they might increase the part of explained variance of annoyance. But the use of these indices in field studies leads to issues. Actually, audio recordings are needed for their calculation, making too tedious their use in large-scale studies. This work proposed a methodology, and its testing, to estimate various psychoacoustic and noise indices from the A-weighted equivalent sound pressure level, LAeq, of different transportation noise sources. The transportation noise sources were urban and suburban road traffic noise, urban railway noise, and aircraft noise in cities close to French international airports. A database of index values has been built from various recordings of these transportation noise sources. From the database, relationships between the various indices and LAeq of the transportation noise sources were built, and satisfactorily tested by cross-validation. These relationships might be of interest for field studies dealing with transportation noise assessment. In the current study, the relationships were used to estimate variables of noise annoyance models based on different psychoacoustic and noise indices. The proposed relationships enabled the assessment of the prediction quality of these models also based on individual noise sensitivity. The comparison of their prediction quality with the one of noise annoyance models solely based on the day-evening-night level, Lden, and of a model based on both noise sensitivity and the day-night level, Ldn, highlighted an improvement of the correlation coefficients between the predicted and the field measured annoyance ratings with a ratio of 2 for the best enhancement. Such a ratio was observed as the correlation coefficient obtained for models solely based on Lden were weak. The results stated the interest of the proposed index relationships to be used in the investigation of improving noise annoyance models in future annoyance field studies.

Lane Detection with Deep Learning: Methods and Datasets

Lane detection problem has been considered as an important computer vision task in autonomous driving. While it has received massive research attention in the literature, the problem is not yet fully solved. In this paper, we present a comprehensive literature review for lane detection, especially those with deep learning models. The latest collection of lane detection datasets is presented. We further fill the research gap by proposing a novel lane detection dataset named MudLane, which focuses on the lane detection task on suburban roads.

Dynamic behaviour of automobile thermoelectric waste heat recovery under different driving cycles

Different types of vehicles often run in different driving cycles, resulting in different dynamic behaviours of the automobile thermoelectric generator. The dynamic performance of a simplified automobile thermoelectric generator under different driving cycles, including HWFET, NEDC, WLTP, and CLTC, is evaluated to guide the application of automobile thermoelectric waste heat recovery by a hybrid transient CFD-TE model. The model is experimentally validated with an average output error of 10.92%, mainly caused by the instrument error. The findings indicate that the response of the dynamic output power of the automobile thermoelectric generator exhibits smoother fluctuations compared to the exhaust temperature, and also displays hysteresis in its response, while the dynamic conversion efficiency experiences sharp fluctuations. The behaviour of the automobile thermoelectric generator in dynamic situations is not entirely proportional to the vehicle speed but is instead dependent on changes in vehicle speed, more specifically, the change of exhaust heat. By comparing the steady and dynamic results, it is found that the steady-state model overestimates the output power but may underestimate the conversion efficiency. The automobile thermoelectric generator is more suitable for vehicles with frequent speed changes, such as passenger cars on urban and suburban roads, to enhance the dynamic output performance.

Assessment of coastal vulnerability to land-based sources of pollution and its application in Apulia, Italy

The present paper deals with the assessment of the coastal vulnerability to pollution sources induced by land-based activities (LBAs). The coastal vulnerability is expressed and evaluated in relation to the land-based activities present in the coastal areas and a new index, namely Coastal Pollution Index from land-based activities (CPI_LBAs), is proposed.The index is calculated considering nine indicators using a transect-based approach. The nine indicators refer to point and non-point sources of pollution, and include: rivers quality status, seaports category, airports category, wastewater treatment facilities/submarine outfalls, aquaculture/mariculture areas, pollution load of urban runoff, type of artisanal/industrial facilities, farm/agriculture areas, type of suburban roads. Each indicator is quantified by means of quantitative scores, whereas the strength of cause-effects relationship is assessed by assigning weights using the Fuzzy Analytic Hierarchy Process approach (F-AHP). The indicators are then aggregated to obtain a synthetic index and classified in five categories of vulnerability.The main findings of this study include: i) the identification of key indicators of coastal vulnerability to LABs; ii) the development of a new index to identify the coastal transects where the effects of the LBAs may be the greatest.The paper explains the methodology for the index computation, and illustrates it with an application in Apulia, Italy. The results demonstrate the feasibility of the index and its usefulness to identify the most critical areas in terms of land pollution sources and to produce a vulnerability map.The application allowed to have a synthetic picture of the threat of pollution from LBAs, facilitating its analysis and the benchmarking between the transects. Regarding the case study area, results show that low vulnerability transects are characterized by small agricultural areas/artisanal activities and small urban areas, whereas in very high vulnerability transects all indicators assume high scores.

Modelling the response times of mobile phone distracted young drivers: A hybrid approach of decision tree and random parameters duration model

Research has shown the detrimental effects of using mobile phones whilst driving, which are more prominent and concerning for young drivers, who are often less experienced and riskier. As such, this study investigates young drivers’ response times when they encounter a safety–critical event on a suburban road whilst using a mobile phone. To collect high-quality trajectory data, the CARRS-Q advanced driving simulator was used. Thirty-two licenced young drivers were exposed to the sudden braking of the lead vehicle in their lane in three driving conditions: baseline (no phone conversation), handheld, and hands-free. Unlike extant studies, this paper proposes a hybrid modelling framework for the response times of distracted drivers. This framework combines a decision tree model and a correlated grouped random parameters duration model with heterogeneity-in-means. While the decision tree model identifies a priori relationship among main effects, the random parameter model captures unobserved heterogeneity and correlation between random parameters. The modelling results reveal that mobile phone distraction impairs response time behaviour for the majority of drivers. However, some drivers tend to respond earlier whilst being distracted, suggesting that the perceived risk of mobile use might have led to an early response, indicating their risk compensation behaviour. Female drivers tend to respond earlier compared to male drivers, indicating their safer and risk-averse behaviour. Overall, mobile phone distraction appears to deteriorate response time behaviour and poses a significant safety concern to drivers and the overall traffic stream unless mitigated.