Roadway Infrastructure Research Articles

Pedestrian Facilities as a Part of Road Infrastructure Resilience in Large Cities in Indonesia

Road infrastructure resilience is critical to reach disaster resilience. Disaster not only occurs and impacts the location outside cities, but also in an area with a large number of the population, like cities. In large cities, pedestrian facilities are the important road infrastructure facilities besides roadway infrastructure, both in the normal daily condition and during evacuation if there is a disaster. Unfortunately, pedestrian facilities in the city are not yet seriously taken into account. The aim of this paper is, first, identification of the availability of pedestrian facilities, second, fulfillment of the facilities based on the regulation in Indonesia, and third, recommendation to implement items of pedestrian facilities based on pedestrians’ opinion. Case study is carried out on two major roads in a large city, Bandung, Indonesia, with a high number of pedestrians because there are various activities happening along the streets, for example, working, studying, shopping, and sightseeing. The method used is an interview, questionnaire, and essential performance analysis, with 18 pedestrian facility indicators — seven hundred and eighty-two pedestrians as respondents involved in this paper. The analysis is done based on respondent demographics and based on significant rate and priority rate of respondents’ opinion. Results indicated that not all of the pedestrian facilities had been implemented according to regulation in Indonesia. Furthermore. based on respondents’ opinion, the most important and priority items of pedestrian facilities to be applied as soon as possible are street lighting, pedestrian ramps for disabled persons, CCTV, and trash bins. Fulfillment of all pedestrian facilities will enhance road infrastructure resilience through reduction of casualty if no disaster occurs and through reduction of risk during evacuation if disaster occurs.

Policy processes and recommendations for Unmanned Aerial System operations near roadways based on visual attention of drivers

Unmanned Aerial Systems (UASs), commonly known as drones, are a rapidly emerging technology with many applications across various commercial, government, and recreational users. Many of these applications have the potential to interact with roadway infrastructure, resulting in potentially risky conflicts between UAS operations and drivers on the roadway. In the United States, policy regulating UAS operations exists at the federal, state, and local levels, but there is little to no regulation specifically related to UAS operations near roadways. The purpose of this study was to evaluate if UAS operations near roadways pose a safety concern by determining if the operations visually distract drivers. In addition, this study sought to develop data-driven policy recommendations to improve the safety of drivers and UAS operators near roadways. To understand how UAS operations near roadways influence the visual attention of drivers, an experiment was designed and conducted in a high-fidelity driving simulator. Thirty participants completed the experiment in the driving simulator and their visual attention was recorded. Analysis of the visual attention results showed that UAS operations draw more visual attention from drivers when they are directly adjacent to the roadside or in a rural environment. Based on the results, a recommended policy to improve safety of UASs for operators and drivers would be to, at a minimum, restrict UAS operations within 7.6 m (25 ft) of the edge of a lane. A procedural overview for implementing legal and effective UAS policy in the United States was developed to navigate the complexities of the evolving UAS policy landscape.

A review of sustainability metrics for the construction and operation of airport and roadway infrastructure

Sustainability has become increasingly important, however, relatively little attention has focused on metrics for the construction and operation of airport and roadway infrastructure. Most attention has focused on buildings, with high profile BREEAM and LEED projects taking center stage. Sustainability is also important in airport and roadway infrastructure projects, which have significant public impact but have a much lower profile than vertical construction when it comes to sustainability. Sustainable infrastructure is important in China and India where new infrastructure is under construction to meet growing and developing economies, and in the US, where infrastructure is in substandard condition and requires reconstruction. The purpose of this paper is to provide an overview and discussion of sustainability rating systems for airport and roadway infrastructure, including both construction and operation. Specific projects that highlight both proven and innovative sustainable practices are included to illustrate the application of these concepts. Finally, the relationship between sustainable transportation infrastructure and resilient transportation infrastructure is addressed since resiliency is of growing interest and there is overlap between these concepts.

Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

The safety, mobility, environmental, energy, and economic benefits of transportation systems, which are the focus of recent connected vehicle (CV) programs, are potentially dramatic. However, realization of these benefits largely hinges on the timely integration of digital technology into upcoming as well as existing transportation infrastructure. CVs must be enabled to broadcast and receive data to and from other CVs [vehicle-to-vehicle (V2V) communication], to and from infrastructure [vehicle-to-infrastructure (V2I) communication], and to and from other road users, such as bicyclists or pedestrians (vehicle-to-other road users communication). Further, the infrastructure and transportation agencies that manage V2I-focused applications must be able to collect, process, distribute, and archive these data quickly, reliably, and securely. This paper focuses on V2I applications and investigates current digital roadway infrastructure initiatives. It highlights the importance of including digital infrastructure investment alongside investment in more traditional transportation infrastructure to keep up with the auto industry push toward increasing intervehicular communication. By studying current CV testbeds and smart-city initiatives, this paper identifies digital infrastructure being used by public agencies. It also examines public agencies limited budgeting for digital infrastructure and finds that current expenditure is inadequate for realizing the potential benefits of V2I applications. Finally, the paper presents a set of recommendations, based on a review of current practices and future needs, designed to guide agencies responsible for transportation infrastructure. It stresses the importance of collaboration for establishing national and international platforms for the planning, deployment, and management of digital infrastructure to support connected transportation systems.

Vibration monitoring of a small concrete bridge using wavelet transforms on GPS data

Small concrete bridges are important roadway infrastructures in Brazil, where they serve as crucial links in the transportation network. Monitoring and maintenance of these bridges are essential for prolonging their lifetimes. The original work presented in this paper is based on the use of global positioning system (GPS) as a trustworthy tool for characterizing the dynamic behavior of small concrete bridges under normal operating conditions. The proposed inspection procedure includes the analysis of only two GPS signals with a method named the phase residual method, which is based on phase interferometry. Analytical methods, namely, the fast Fourier transform, continuous wavelet transform, and Chebyshev, are effective signal processing techniques for visualizing variations in system frequencies. The object of the study was the bridge over the Jaguari River, which is located in Brazil. The description of the dynamic behavior of the middle span of the concrete bridge was obtained by analyzing data obtained at a sampling frequency of 100 Hz collected for only 1 min. The results provided a clear picture of the energy distribution in the time–frequency plane during forced vibration: (1) the maximum deformation of the main span was pronounced at 3.5–8 mm; (2) the vibration frequency ranged between 4 and 8 Hz; and (3) the values obtained coincided with the values indicated by regular the monitoring sensors, such as accelerometers and transducers of displacements. The advantages of the use of GPS and the contribution to the geotechnical short- or long-term monitoring of road bridges are the use of a reference outside the structure, use under inclement weather conditions, and lack of disruption of traffic.

Structural and environmental predictors of presence and abundance of tri-colored bats in Texas culverts

AbstractThe tri-colored bat (Perimyotis subflavus) is being reviewed for listing under the Endangered Species Act by the United States Fish and Wildlife Service. It is also listed as a species of greatest conservation need by the Texas Parks and Wildlife Department due to its susceptibility to white-nose syndrome (WNS) in other states. Several colonies of hibernating tri-colored bats have been documented roosting in culverts. Culverts are widespread in Texas as part of roadway infrastructure; thus, our objective was to understand and quantify which structural and environmental factors best explain culvert use and abundance of hibernating tri-colored bats in Texas. We selected and surveyed 207 culverts for presence of tri-colored bats using the Generalized Random Tessellation Stratified (GRTS) design and opportunistic sampling across 10 of 12 Texas level III ecoregions during the winters of 2016–2017 and 2017–2018. We recorded environmental and structural features of culverts at each site. We used a zero-inflated Poisson regression to identify which culvert features best explained presence and abundance of hibernating tri-colored bats. We found that number of culvert sections predicted presence of tri-colored bats. We also found that abundance of tri-colored bats was influenced by length of culvert, elevation, number of sections, portal height, portal obstruction, aspect, external VPD, external temperature, and NDVI. With the current threats to tri-colored bat populations, there is a need to consider management of culvert roosts. In addition, it is imperative to further investigate the potential susceptibility to WNS of culvert-roosting bats at more southern latitudes for local and regional planning efforts.

Frequency and Severity of Tree and Other Fixed Object Crashes in Florida, 2006—2013

Roadside trees provide benefits to drivers such as traffic calming, roadway definition, and driver stress reduction. However, trees are also one of several roadway infrastructure elements commonly involved in single-vehicle crashes. In this study, Florida Highway Saftey and Motor Vehicle records were analyzed to: evaluate the relative frequency of tree-related crashes compared to other fixed-object crashes; assess the impact of roadway-, vehicle-, and driver-related factors on tree crash frequency; and compare the severity of tree crashes relative to other single-vehicle crashes. In accessing 3,033,041 crash records from 2006 to 2013 (all complete years), we identified 565,303 single-vehicle accidents (10.5%) and 47,341 tree-related accidents (1.6%). Trees were the fourth most common fixed object hit in urban single-vehicle accidents and the second most common fixed object hit in rural single-vehicle accidents. Driver gender, vehicle type, light conditions, weather conditions, and land use all were correlated with the frequency. Additionally, the injuries associated with tree crashes were more severe than all other single-vehicle crash types except vehicle rollovers.

A literature synthesis of LiDAR applications in transportation: feature extraction and geometric assessments of highways

Interest in using Light Detection and Ranging (LiDAR) technology in Transportation Engineering has grown over the past decade. The high accuracy of LiDAR datasets and the efficiency by which they can be collected has led many transportation agencies to consider mobile LiDAR as an alternative to conventional tools when surveying roadway infrastructure. Nonetheless, extracting semantic information from LiDAR datasets can be extremely challenging. Although extracting roadway features from LiDAR has been considered in previous research, the extraction of some features has received more attention than others. In fact, for some roadway design elements, attempts to extract those elements from LiDAR have been extremely scarce. To document the research that has been done in this area, this paper conducts a thorough review of existing studies while also highlighting areas where more research is required. Unlike previous research, this paper includes a thorough review of the previous attempts at data extraction from LiDAR while summarizing the detailed steps of the extraction procedure proposed in each study. Moreover, the paper also identifies common tools and techniques used to extract information from LiDAR for transportation applications. The paper also highlights common limitations in existing algorithms that could be improved in future research. This paper represents a valuable resource for researchers and practitioners interested in knowing the current state of research on the applications of LiDAR in the field of Transportation Engineering while also understanding the opportunities and challenges that lie ahead.

Variable impact transportation (VIT) model for energy and environmental impact of hauling truck operation

Fuel economy and emissions of heavy-duty trucks greatly vary based on vehicular/environmental conditions. Large-scale infrastructure construction projects require a large amount of material/equipment transportation. Single-parameter generic hauling models may not be the best option for an accurate estimation of hauling contribution in life cycle assessment (LCA) involving construction projects; therefore, more precise data and parameterized models are required to represent this contribution. This paper discusses key environmental/operational variables and their impact on transportation of materials and equipment; a variable-impact transportation (VIT) model accounting for these variables was developed to predict environmental impacts of hauling. The VIT model in the form of multi-nonlinear regression equations was developed based on simulations using the U.S. Environmental Protection Agency (EPA)’s Motor Vehicle Emission Simulator (MOVES) to compute all the impact categories in EPA’s TRACI 2.1 and energy consumption of transportation. Considering actual driving cycles of hauling trucks recorded during a pavement rehabilitation project, the corresponding environmental impacts were calculated, and sensitivity analyses were performed. In addition, an LCA case study based on historical pavement reconstruction projects in Illinois was conducted to analyze the contribution of transportation and variability of its impacts during the pavements’ life cycle. The importance of vehicle driving cycles was realized from simulation results. The case study results showed that considering driving cycles using the VIT model could increase the contribution of hauling in total life cycle Global Warming Potential (GWP) and total life cycle GWP itself by 2–4 and 3–5%, respectively. In addition to GWP, ranges of other hauling-related impact categories including Smog, Ozone Depletion, Acidification, and Primary Energy Demand from fuel were presented based on the case study. Ozone Depletion ranged from 9 to 45%, and Smog ranged from 11 to 48% of the total relevant life cycle impacts. The GWP contribution of hauling in pavement LCA ranged between 5 and 32%. The results indicate that the contribution of hauling transportation can be significant in pavement LCA. For large-scale roadway infrastructure construction projects that need a massive amount of material transportation, high fidelity models and data should be used especially for comparative LCAs that can be used as part of decision making between alternatives. The VIT model provides a simple analytical platform to include the critical vehicular/operational variables without any dependence on an external software; the model can also be incorporated in those studies where some of the transportation activity data are available.

Injury prevalence and safety habits of boda boda drivers in Moshi, Tanzania: A mixed methods study.

BackgroundTraffic crashes are a major cause of global morbidity and mortality disproportionately affecting low- and middle-income countries (LMICs). Motorcycle taxi (boda boda) drivers are particularly vulnerable because they are exposed to traffic risks with limited safety equipment. This study aims to characterize injury prevalence and safety habits among boda boda drivers, as well as ways to improve road traffic safety in LMICs.MethodsA cross-sectional mixed methods study was conducted with 300 boda boda drivers between 24 March and 3 April 2014 in urban Moshi, Tanzania. A convenience sample of participants was drawn from 25 of 58 registered boda boda stands and 2 of 31 unregistered stands. Data were analyzed using R, and content thematic analysis was performed and agreed upon by three investigators. Logistic regression models were used to evaluate the association between boda boda characteristics and injury risk.ResultsIn total, 300 drivers participated, of whom 148 (49.3%) had experienced a crash during their lifetime, and 114 (77.0%) sustained at least one injury. Only 27 of those injured (23.4%) were hospitalized. Of all participants, 220 (73.3%) reported consistent helmet usage, despite 285 participants (95.0%) agreeing that helmet usage reduces injury severity. From the 280 helmets observed, 231 (82.5%) were either damaged or fit improperly. Having a cracked helmet was associated with higher risk of being involved in a traffic crash. Owning a helmet with a proper fit was associated with reduced risk for a traffic crash (OR = 0.06) and road traffic injuries (OR = 0.07). A thematic analysis of boda boda drivers’ suggestions to increase road safety identified four intervention areas: 1) roadway infrastructure and traffic regulation, 2) road user attitudes and safe driving behaviors, 3) education and training, and 4) law enforcement.ConclusionOur study demonstrates boda boda drivers’ safety behaviors and identifies four intervention areas that can be leveraged to increase overall road traffic safety. Unfortunately, while boda boda drivers are aware of ways to improve safety, adherence to safety habits remains low. Successful multi-sectoral interventions are needed to improve road safety for boda boda drivers in Tanzania.

Damage to Roadway Infrastructure from 2016 Central Italy Earthquake Sequence

The region of the central Apennines affected by the 2016 earthquake sequence has numerous towns, villages, and isolated dwellings connected by local secondary roads and a few state highways. The roadway network includes several bridges that are important to the economy of the region and play an important role in the post-earthquake resilience of local communities. Within this network, 12 bridges and a rockfall protection tunnel were inspected in coordination with local officials, with relatively cursory reconnaissance of most of the remainder of the network. All inspected reinforced concrete and steel–concrete composite bridges performed adequately. Two historic masonry bridges near Amatrice and Tufo suffered significant damage after the 24 August 2016 main shock, and collapsed after the 30 October 2016 event. Recovery strategies related to the bridge collapse near Amatrice, where two temporary bridges were built within 10 days from the first main shock in August, are discussed. An inspected rockfall protection tunnel experienced earthquake pounding effects.

Measuring the accessibility of critical facilities in the presence of hurricane-related roadway closures and an approach for predicting future roadway disruptions

Roadway closures magnify the adverse effects of disasters on people since any type of such disruption increases the emergency response travel time (ERTT), which is of central importance for the safety and survival of the affected people. Especially in the State of Florida, high winds due to hurricanes, such as the Hurricane Hermine, lead to notable roadway disruptions and closures that compel special attention. As such, in this paper, the accessibility of emergency response facilities, such as police stations, fire stations and hospitals in the City of Tallahassee, the capital of Florida, was extensively studied using real-life data on roadway closures during Hurricane Hermine. A new metric, namely Accessibility Decrease Index, was proposed, which measures the change in ERTT before and in the aftermath of a hurricane such as Hermine. Results clearly show those regions with reduced emergency response facility accessibility and roadways under a disruption risk in the 1-week window after Hermine hit Tallahassee. City officials can pinpoint these critical locations for future improvements and identify those critical roadways, which are under a risk of disruption due to the impact of the hurricane. This information can be utilized to improve emergency response plans by improving the roadway infrastructure and providing alternative routes to public.

A Kinect-Based Approach for 3D Pavement Surface Reconstruction and Cracking Recognition

Pavement surface distress conditions are critical inputs for quantifying roadway infrastructure serviceability. Numerous computer-aided automatic examination techniques have been deployed for pavement distress condition assessments, such as digital image processing methods. However, their effectiveness and applicability are impeded due to information losses in 2-D image combination processes or extremely high costs in 3-D geo-referenced data set. In this paper, a cost-effective Kinect-based approach is proposed for 3-D pavement surface reconstruction and cracking recognition. We propose a comprehensive computational solution for the detection and recognition of pavement distress feature identification. Various cracking measurements such as alligator cracking, traverse cracking, longitudinal cracking, and so on. are identified and recognized for their severity examinations based on associated geometrical features. The experimental results indicate that this method is effective in reducing data collection costs and extracting analytical information on pavement cracking measurements. The research findings confirm that the proposed approach provides a viable, applicable solution to an automatic pavement surface condition detection and evaluation. The proposed methodology is transferable for pavement surface reconstruction and distress condition detection based on the other 3-D cloud point data. It provides an alternative inexpensive complement to existing pavement examination methodologies.

Modeling Free-flow Speeds on Undivided Roads in Mixed Traffic with Weak Lane Discipline

In developing countries like India, transportation systems are characterized by limited roadway infrastructure and lack of operation and management experience. Hence, there exists a need to evaluate a performance indicator that reflects the current level of service (LOS) of a road facility. Free-flow speed (FFS) is a key parameter used to express LOS assessment. The objective of this study is to develop FFS prediction models for undivided roads with mixed traffic conditions in both urban and rural settings in India. Traffic data were collected from two-way two-lane undivided roads in southern India during free-flow traffic conditions using videographic method. Various class-specific and site-specific characteristics, such as vehicle class, subclass, carriageway width, link length, number of side roads, lateral clearance, land use type, and area type, were investigated and their influence on FFS evaluated. Statistical tests assessed the variations of obtained FFS with different vehicle-specific and site-specific factors. Free-flow prediction models were developed using linear regression method. The developed models show that FFS increases with greater carriageway width, lateral clearance, and link length, and decreases with increase in number of side roads. In general, FFS is higher in rural areas than urban areas. Similarly, open areas have higher FFS than residential, institutional, and commercial areas. The model can be used to predict FFS of undivided roads if site-specific and vehicle-specific data are known. This study finds interesting applications in capacity and LOS analysis, accident analysis, and before-and-after studies of road improvement schemes.

Experimental measurement and microstructure-based simulation of thermal conductivity of unbound aggregates

Unbound aggregates are widely used for roadway and railway infrastructure. The thermal conductivity of unbound aggregates is important for predicting temperature field and thermal stress of structures. This paper developed laboratory measurement methods and microstructure-based simulation models to evaluate effective thermal conductivity of unbound aggregates. An image-aided method was used to randomly generate three-dimensional (3-D) geometry of aggregate. The aggregate particles were packed with discrete element method and then imported into finite element model for steady heat transfer analysis. Laboratory experiments were conducted to measure temperature profiles in the specimens of unbound aggregates and determine effective thermal conductivity through back-calculation. The effective thermal conductivity measured from the experiment was used to validate the results obtained from the simulation model. The analysis results show that smaller aggregate with the size of 2.36–4.75 mm has the smaller porosity and the greater thermal conductivity as compared to the aggregates with the size of 4.75–9.5 mm. However, the effective thermal conductivity of unbound aggregates is affected by volume fractions of the components and the contacts between aggregate particles. When the original rock is crushed into aggregates, the decrease in thermal conductivity is more significant for the rock with the higher thermal conductivity. The effective thermal conductivity could be underestimated if two-dimensional (2-D) cross sections of 3-D models were used in the simulation. The study results can be used for better consideration of thermal responses in the design and analysis of roadway and railway structure.

Operational Effects of the Displaced Partial Cloverleaf Interchange

As the roadway infrastructure in the United States evolves, transportation agencies continue to seek effective interchange alternatives that can accommodate site-specific needs, such as high through and turning volumes, or optimized traffic signal operations (by minimizing the number of traffic signal phases). In recent years, innovative interchanges such as the diverging diamond have enabled transportation agencies to explore unique designs compatible with their specific needs. This paper introduces a new innovative interchange, known as a displaced partial cloverleaf (DPC) interchange. This unique interchange is characterized by a single intersection, six free-flow movements, only four movements controlled by the solitary traffic signal, an intersection location that can be shifted, and only 12 conflict points. The flexible intersection location can help an agency improve corridor progression, eliminate conflicting queues from nearby intersections, or enhance/maintain the bridge structure without compromising the intersection operations. A microsimulation study was conducted to compare the operational performance of a DPC interchange to that of a four-quadrant type B partial cloverleaf (PARCLO B-4Q) interchange, which is very similar in layout to a DPC interchange. Results show significant decreases in experienced travel time for left-turning vehicles with the DPC. The DPC interchange is recommended as an alternative option for interchanges with very heavy left-turning onramp demand in combination with heavy crossroad demand.

Cruising and on-street parking pricing: A difference-in-difference analysis of measured parking search time and distance in San Francisco

When on-street parking is scarce, the cost of parking includes the extra time and fuel spent searching for a parking space (or cruising). Cruising also unnecessarily contributes to local congestion, vehicle emissions, air pollution, and climate change. The theoretical literature shows that these social costs can be reduced, or even eliminated, if high-quality information on the demand for and supply of parking is used to set parking prices at optimal levels. Not surprisingly, cities plagued by parking shortages, congested streets, and limited financial resources are interested in parking policies that reduce cruising and improve the efficient use of their existing parking and roadway infrastructure. The current study sheds light on the effect of the San Francisco parking pricing program (known as SFpark) on curbside parking search time and distance in urban neighborhoods on non-commuter parking. The study differs from previous empirical evaluations of similar parking pricing programs in its use of direct field measurements of parking search time and distance, rather than simulated data or proxy variables, such as parking availability. We use generalized mixed effect difference-in-difference models with data collected before and after the implementation of SFpark in both treatment and control areas to estimates effects of the San Francisco smart parking project, most importantly the demand responsive parking pricing scheme. The models control for time effects by using data from a separate control area, as opposed to using variables such as block face parking price and employment. The results suggest a significant reduction in average parking search time and distance due to SFpark. Average parking search time and distance declines by approximately 15% and 12%, respectively, from the control to the treatment areas.

Factors influencing noncompliance with bicycle passing distance laws

Many jurisdictions around the world have implemented laws to require a minimum distance when motor vehicles pass cyclists, but research into the factors influencing passing distances has produced inconsistent results, indicating the need for future research. This study examined the factors influencing motorists’ compliance with a legislated bicycle passing distance rule in Queensland, Australia. Unlike the earlier studies, which used volunteer riders to record passing events, this study used a naturalistic study design to record passing events where none of the motorists or the cyclists were aware of being studied. As a result, this study captured the ‘true’ driving and riding behaviours during passing events. The likelihood of non-compliance was greater on higher (70–80 km/h speed limits) and lower (40 km/h) speed roads than 60 km/h roads, at curved road sections, and on roads with narrower traffic lanes. Rider characteristics (age, gender, helmet status, type of clothing, type of bicycle, and individual or group riding) had no statistically significant association with compliance status. The findings indicate that efforts to improve cyclist safety during overtaking events should focus on non-rider related factors, such as roadway infrastructure characteristics.

Here there be dragons, a pre-roadmap construct for IoT service infrastructure

The major challenges facing the 21st century world demands disruptive technology based solutions. One of the most promising exponential technology set to address world challenges is the Internet of Things (IoT) based Trillion Sensor System (TSS). The IoT supports many revolutionary commercial and societal solutions including wearable or unobtrusive medical sensors, Industry 4.0, power and water grids, smart cities, food production, education, transportation and roadway infrastructure needs. However, to support these solutions the current IoT infrastructure needs improved spectrum and the use of between one to ten Trillion Sensors (TS). The development of a robust IoT based TSS infrastructure would create an addition to world GDP equal to that of the U.S. GDP to double the worlds GDP. This new IoT based TSS would create a high paying job base that will form a new vibrant world middle class and an abundant economy. Yet while much is written about the ability of the IoT to transform society little effort is focused on its infrastructure. If this is true there is cause for concern. We add to the literature by developing a precursor road mapping construct which focuses on the service sector and supports 3rd generation road mapping techniques. We utilize the emerging IoT TSS technology base as our case study. We utilize the best thoughts of hundreds of experts from three organizations focused on accelerating IoT TSS road mapping efforts.

Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models

The objective of this study was to investigate the relationship between bicycle crash frequency and their contributing factors at the census block group level in Florida, USA. Crashes aggregated over the census block groups tend to be clustered (i.e., spatially dependent) rather than randomly distributed. To account for the effect of spatial dependence across the census block groups, the class of conditional autoregressive (CAR) models were employed within the hierarchical Bayesian framework. Based on four years (2011-2014) of crash data, total and fatal-and-severe injury bicycle crash frequencies were modeled as a function of a large number of variables representing demographic and socio-economic characteristics, roadway infrastructure and traffic characteristics, and bicycle activity characteristics. This study explored and compared the performance of two CAR models, namely the Besag’s model and the Leroux’s model, in crash prediction. The Besag’s models, which differ from the Leroux’s models by the structure of how spatial autocorrelation are specified in the models, were found to fit the data better. A 95% Bayesian credible interval was selected to identify the variables that had credible impact on bicycle crashes. A total of 21 variables were found to be credible in the total crash model, while 18 variables were found to be credible in the fatal-and-severe injury crash model. Population, daily vehicle miles traveled, age cohorts, household automobile ownership, density of urban roads by functional class, bicycle trip miles, and bicycle trip intensity had positive effects in both the total and fatal-and-severe crash models. Educational attainment variables, truck percentage, and density of rural roads by functional class were found to be negatively associated with both total and fatal-and-severe bicycle crash frequencies.