Road Closures Research Articles

Correlating Machine Learning Classi cation of Traf c Camera Images with Snow-related Vehicular Crashes in New York State

Millions of motor vehicle crashes and tens of thousands of resulting deaths occur each year in the United States. While it is well known that wintry conditions make driving more difficult and dangerous, it is difficult to quantify and communicate the threat to motorists, especially in real time. This proof-of-concept research uses machine learning (ML) to approach this problem in a new way by creating a ML model that can identify snow on the road froma traf c camera image. This information is coupled with the number of coincident vehicular crashes to provide detailedconsideration of the impact of snow on the road to motorists and transportation agency decision-makers. It was foundthat, during meteorological winter, when the ML model determined there to be snow on the road in a traf c camera image, the chance of a vehicular crash pairing with that traf c camera increased by 61%. The systems developed as part of this research have potential to assist roadway of cials in assessing risk in real time and making informed decisionsabout snow removal and road closures. Moreover, the implementation of in-vehicle weather hazard information could promote driver safety and allow motorists to adjust their driving behavior and travel decision making as well.

End of the road: Short-term responses of a large mammal community to forest road decommissioning

Anthropogenic disturbances are increasing worldwide, causing wildlife habitat loss, alteration, and fragmentation. In Canada, the decommissioning of linear anthropogenic structures is identified as a promising tool to restore the habitat of threatened populations of boreal caribou (Rangifer tarandus caribou) by reducing food availability for alternate prey and decreasing encounter probabilities with predators. In this study, we monitored the use of 40 km of decommissioned forest roads by caribou, gray wolves (Canis lupus), black bears (Ursus americanus), and moose (Alces americanus) 1–3 years after reclamation, using 232 motion-activated camera traps. We compared four additive treatments (meaning that each successive treatment included the treatment prior): closing the road to human access, decompacting its soil, planting black spruce (Picea mariana) trees, and adding enriched soil. We assessed the influence of treatments, use by other large mammals, and characteristics of the surrounding environment on road use by the four species. Caribou used the planted treatment (which also included closing and decompacting) more than the closed-only (reference) treatment, but treatments did not influence the use of decommissioned roads by bears and moose. We could not assess the use of treated roads by wolves because of low sample size. Road use by caribou declined with local moose density, but increased with local bear density. Caribou were observed more frequently on roads surrounded by regenerating and mature coniferous stands; caribou also preferentially used roads surrounded by wetlands. Our results suggest that the treatment combining road closure, soil decompaction, and tree planting could be beneficial to caribou, highlighting the relevance of including active restoration efforts in caribou conservation programs. We recommend that such a treatment be added to road decommissioning protocols for the conservation of caribou, alongside broad-scale habitat protection.

Seismic Resilience Assessment Strategy for Social and Sustainability Impact Evaluation on Transportation Road Network: A Seismic Liquefaction-Induced Damage Application

Transport networks play a critical role for living communities, as they facilitate the exchange of people and goods and foster economic growth. Improving their resilience against seismic hazards, among which liquefaction is by far one of the most significant and complex, is consistent with most of the Sustainable Development Goals pinpointed by the United Nations’ Agenda. In this paper, an original methodological framework, combining innovative Geo-statistical approaches to analyze soil properties, prediction models for soil liquefaction, and calibrated transport demand models providing the social and economic cost associated with seismic-induced road damages and closures within a renewed Geographical Information Systems (GIS) workspace, is proposed. In particular, based on traditional risk assessment evaluation, an innovative approach to evaluate the exposure in terms of economic loss due to lack of accessibility is presented. The methodology is applied to a district area in northern Italy that underwent a recent seismic event that caused several soil liquefaction phenomena. Results provided by a sensitivity analysis on a stochastic (return period) basis are derived: as the seismic intensity increases, the total social costs increase, but the trend of the rates due to traffic delays and the loss of accessibility are irregular. Although further simulation scenarios need to be undertaken, the proposed methodology seems to provide an effective planning tool to evaluate preventive strategies aimed at improving the resilience of transport networks against liquefaction risk.

Fenomena Penutupan Jalan Umum Untuk Acara Kedukaan Di Wilayah Kampung Kei Kota Jayapura (The phenomenon of Closing Public Roads for Grief Events in Kei Village Area Jayapura City)

The road closures were carried out by residents of Kei Abepura Village, Yobe Village, and Abepura District when there was grief, as well as during worship for mourning activities of residents living around Grilyawan Street of Abepura Kei Village, so that the road for vehicles from the direction of Keerom, Abe Pantai and Koya was diverted turn to the direction to the Mental Hospital or through the small alleys in the upper complex of the Kei Abepura village to get to the Abepura circle area, for the opposite direction, namely from the Abepura circle, they have to pass Youtefa to get out to Tanah Hitam, Abe Pantai, Koya, and Keroom. This research purposes to find out the road closure model, the general public’s response, and the occurrence of the road closures carried out by the residents of Kei Village when there is grief in the residents of Kei Village, Abepura District, Jayapura City. This study uses a qualitative research method with the paradigm of social phenomenology. The results show that road closures for mourning activities are an alternative that is taken due to the absence of residents' yards which leads to various perceptions from the general public about the closure of provincial roads for personal activities. It takes clear rules issued by the local government regarding the use of public facilities for personal or group interests. It also requires public awareness about prioritizing the public interest over personal or group interests.

Fieldwork opportunities and challenges in Palestinian refugee camps in the West Bank, occupied Palestinian territory, and in Jordan: lessons learned from a Palestinian adolescent reproductive health cross-sectional study

BackgroundPalestinian refugee camps have lasted for more than 70 years. Generally the residents are poor and the camps are crowded and politically unstable, with a range of social, cultural, and economic constraints. Studying the adolescent reproductive health of girls in refugee camps is crucial yet challenging, primarily because of its sensitive nature. We use experiences from a cross-sectional study of Palestinian adolescent reproductive health to discuss methodological challenges and opportunities for researching this subject in the context of refugee camps. MethodsWe completed a household survey in Palestinian refugee camps in the West Bank (19 camps) and Jordan (ten camps) from June to September, 2019. Data from 2949 adolescent girls aged 15–18 years (1443 from the West Bank and 1506 from Jordan) were collected in relation to menarche and menstruation, mental health, and nutrition. A fingerprick haemoglobin test for anaemia was conducted among participating girls. Data were collected by women fieldworkers using electronic tablets. Written consent was obtained from girls and their mothers or a female caregiver for both the survey and the blood tests. Consent to enter the household was obtained first, then consent from girls. FindingsResponse rates at the household level were 3573 (87%) of 4103 and 3210 (74%) of 4314 for the West Bank and Jordan, respectively, and for girls were 1443 (95%) of 1522 and 1506 (99%) of 1526 for the West Bank and Jordan, respectively. This seemed to suggest a higher level of trust and willingness to participate in the West Bank compared with Jordan at the household level, although this seemed almost no problem for the girls as long as their caregivers had agreed. During data collection, we encountered fieldwork challenges at political, social, cultural, and structural levels. When recruiting participants, fieldworkers used a random-walk procedure, as it was hard for them to track their route in the narrow, unstructured, and unidentified camp roads and alleyways, which caused delays and disruption to the process. Crowded households posed an ethical challenge in securing girls’ privacy, particularly in more conservative Jordan. Furthermore, we encountered resistance to fingerprick blood tests owing to fear of contamination, particularly in the more politically unstable camps in the West Bank. The political situation also affected fieldwork in the West Bank by limiting working hours owing to road closures and Israeli army incursions. Despite this, fieldworkers were able to develop a good rapport with participants, especially when the fieldworkers themselves were camp dwellers. Engagement and involvement with local organisations was essential for research teams to gain the trust of families and girls, enabling valuable and credible data collection. InterpretationWhen conducting studies in Palestinian refugee camps, especially on sensitive issues, it is crucial to know the camp well, obtain assistance from local organisations to acquaint and familiarise the population with the research, and to use trained fieldworkers from the local community. Finally, and most importantly, it is essential to be flexible and to have contingency plans that include extra time, personnel, and resources that may be needed to overcome unforeseen circumstances. FundingInternational Development Research Centre (IDRC), Ottawa, Canada.

POLITICAL WILL MAYOR OF KEDIRI IN OVERCOMING COVID-19 PANDEMICS

The COVID-19 pandemic urges the central government to develop various policies, rules, standards, guidelines, and criteria to deal with COVID-19, but the problem lies with local governments. Therefore, regional heads are required to use their political will to control COVID-19 cases and overcome the impacts caused by the COVID-19 pandemic. This study aims to analyze the political will of the Mayor of Kediri in overcoming the COVID-19 pandemic through the seven components of Brinkerhoff's political will. Using a qualitative approach which was analyzed descriptively. Data collection techniques were carried out by in-depth interviews and focus group discussions. The results show that the Mayor of Kediri has tried to use his power and authority to the maximum extent possible on the six components of political will, namely government initiative; priority/choice of policy; mobilization of stakeholders; public commitment and allocation of resources; continuity of effort; and learning and adaptation. But unfortunately weak in the component of the application of credible sanctions. The Mayor of Kediri feels the need to form experts in the field of law to improve the quality and quantity of legislation and conduct academic studies and discussions before deciding policies in the era of the COVID-19 pandemic to be effective and solution-oriented In addition, consistency in enforcing the rules, not half-way in carrying out road closure policies that seem just a formality is also a concern of researchers.

Catch Me if You Can

Catch Me if You Can

Prediction and prevention of pandemics via graphical model inference and convex programming

Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge assuming that there are N census blocks (nodes) in the city, and given an initial infection at any set of nodes, e.g. any N of possible single node infections, any N(N-1)/2 of possible two node infections, etc, what is the probability for a subset of census blocks to become infected by the time the spread of the infection burst is stabilized? (2) Prevention Challenge What is the minimal control action one can take to minimize the infected part of the stabilized state footprint? To answer the challenges, we build a Graphical Model of pandemic of the attractive Ising (pair-wise, binary) type, where each node represents a census tract and each edge factor represents the strength of the pairwise interaction between a pair of nodes, e.g. representing the inter-node travel, road closure and related, and each local bias/field represents the community level of immunization, acceptance of the social distance and mask wearing practice, etc. Resolving the Inference Challenge requires finding the Maximum-A-Posteriory (MAP), i.e. most probable, state of the Ising Model constrained to the set of initially infected nodes. (An infected node is in the + , 1 state and a node which remained safe is in the - , 1 state.) We show that almost all attractive Ising Models on dense graphs result in either of the two possibilities (modes) for the MAP state: either all nodes which were not infected initially became infected, or all the initially uninfected nodes remain uninfected (susceptible). This bi-modal solution of the Inference Challenge allows us to re-state the Prevention Challenge as the following tractable convex programming: for the bare Ising Model with pair-wise and bias factors representing the system without prevention measures, such that the MAP state is fully infected for at least one of the initial infection patterns, find the closest, for example in l_1, l_2 or any other convexity-preserving norm, therefore prevention-optimal, set of factors resulting in all the MAP states of the Ising model, with the optimal prevention measures applied, to become safe. We have illustrated efficiency of the scheme on a quasi-realistic model of Seattle. Our experiments have also revealed useful features, such as sparsity of the prevention solution in the case of the l_1 norm, and also somehow unexpected features, such as localization of the sparse prevention solution at pair-wise links which are NOT these which are most utilized/traveled.

Effects of different aggregate and conductive components on the electrically conductive asphalt concrete's properties

ABSTRACT Since ancient times, the closure of roads due to ice and snow accumulation is one of the most critical problems of highway transportation. There are two different approaches to prevent icing on the road: passive and active methods. In this study, the most efficient aggregate type and the asphalt mixture combination containing electrically conductive components was determined for electrically conductive asphalt pavements which are considered among the active anti-icing methods. For this purpose, a two-stage experimental study was carried out. In the first stage, asphalt concrete (CAC) specimens with 25 different aggregate and conductive component contents were produced and their properties such as volumetric resistivity, the temperature variations under constant (30 Volt) electrical direct current (DC), heating and cooling times and thermal conductivity were determined. Marshall asphalt design was also performed for three series (2.1; 4.1, and 4.3) for the best electrical and thermal conductivity properties in the second stage of the study. Volumetric resistivity tests were performed and temperature variations under constant voltage (30 V) were determined on the Marshall specimens. As a result of the study, environmentally friendly asphalt specimens with wearing layer having high electrical conductivity properties were developed. It was concluded that not only the conductive components but also the aggregate type was reasonably effective in terms of anti-icing performance and CAC specimens could exhibit behaviour close to traditional asphalt properties.

Impact of the COVID-19 Epidemic on Population Mobility Networks in the Beijing–Tianjin–Hebei Urban Agglomeration from a Resilience Perspective

As an important symbol and carrier of regional social and economic activities, population mobility is a vital force to promote the re-agglomeration and diffusion of social and economic factors. An accurate and timely grasp on the impact of the COVID-19 epidemic on population mobility between cities is of great significance for promoting epidemic prevention and control and economic and social development. This study proposes a theoretical framework for resilience assessment, using centrality and nodality, hierarchy and matching, cluster, transmission, and diversity to measure the impact of the COVID-19 epidemic on population mobility in the Beijing–Tianjin–Hebei (BTH) urban agglomeration in 2020–2022, based on the migration data of AutoNavi and social network analysis. The results show that the COVID-19 epidemic had different impacts on the population network resilience of the BTH urban agglomeration based on the scale and timing. During the full-scale outbreak of the epidemic, strict epidemic prevention and control measures were introduced. The measures, such as social distancing and city and road closure, significantly reduced population mobility in the BTH urban agglomeration, and population mobility between cities decreased sharply. The population mobility network’s cluster, transmission, and diversity decreased significantly, severely testing the network resilience. Due to the refinement of the epidemic control measures over time, when a single urban node was impacted, the urban node did not completely fail, and consequently it had little impact on the overall cluster, transmission, and diversity of the population mobility network. Urban nodes at different levels of the population mobility network were not equally affected by the COVID-19 epidemic. The findings can make references for the coordination of epidemic control measures and urban development. It also provides a new perspective for the study of network resilience, and provides scientific data support and a theoretical basis for improving the resilience of BTH urban agglomeration and promoting collaborative development.

The underestimated role of the transportation network: Improving disaster & community resilience

Improving community resilience relies on our ability to evaluate and understand the direct and indirect role of the transportation network. This requires losing our fixation with network functionality in favour of an approach that evaluates whether the network is truly serving the community’s needs. In this paper, we present such an approach. With the understanding that sufficient and equitable access to amenities is key to community resilience, we leverage open-source data and routing algorithms to simulate road and service closures under various hazard scenarios. Among our methodological contributions is the ability to efficiently modify entire transportation networks to reflect hazard damage and identify isolated communities. This makes integrating hazard simulation with access and equity evaluations practicable on a large scale. We illustrate this approach in three cities. Ultimately, a broader view of the transportation network’s role will better support communities prepare for and respond equitably when a disaster occurs.

Multi-class hazmat distribution network design with inventory and superimposed risks

Transportation and inventory are essential to hazardous materials logistics, while different classes of hazardous materials are often transported over a network simultaneously. Despite their in-transit and storage incompatibility, the superimposed risks among different materials, which results from possible chemical reaction once accidents (e.g., leakage, explosion) happen, further complicate the comprehensive plans. In this study, we introduce a new multi-class hazmat distribution network design problem with inventory and superimposed risks (MHND) in a multi-echelon supply chain, where the planning of locations, inventory, and routes are made together. The long-term detour cost/risk and the cyclic time windows penalty costs under the time-dependent (periodic) road closure policy are explicitly formulated. We further propose a new population-based risk definition that evaluates the risk for the population at any location and any time with respect to its multi-class hazmat logistics system. In particular, to capture the interactions between different types of materials, we introduce risk superposition coefficients to capture possible superimposed risks among different hazmat that accommodate a general system with more than two hazmat types. We develop a knowledge-based NSGA-II algorithm with cyclic dissimilarity-based elitist selection (NSGA-II-CD) to solve the problem. The devised cyclic dissimilarity-based elitist selection (CD) operator can tackle the issue of speeding proliferation, which greatly improves the solution quality. Our model is applied to a metropolitan-wide real-world case study in Guangzhou, China. The results suggest that, from the perspective of the traffic management sector, the periodic road closures policy in Guangzhou could be possibly upgraded to a full-time prohibition. Moreover, the results provide the following insights to authorities (1) there is a positive convex relation between risk minimization and risk equilibration. The authorities should not try to find a perfect distribution of risk, and they should make a trade-off between the risk equity and total exposed risk; (2) there is a positive correlation between the level of service and total risk. Thus, in practice, the agencies should make a trade-off between economic viability of the system, exposed risk, and maintaining good service for customers; and (3) the interactions between different types of hazmat considerably affect the distribution network design; specifically, the route overlapping ratio for different types of hazmat decreases when their interactions intensify.

Flood Detection in Urban Areas Using Satellite Imagery and Machine Learning

Urban flooding poses risks to the safety of drivers and pedestrians, and damages infrastructures and lifelines. It is important to accommodate cities and local agencies with enhanced rapid flood detection skills and tools to better understand how much flooding a region may experience at a certain period of time. This results in flood management orders being announced in a timely manner, allowing residents and drivers to preemptively avoid flooded areas. This research combines information received from ground observed data derived from road closure reports from the police department, with remotely sensed satellite imagery to develop and train machine-learning models for flood detection for the City of San Diego, CA, USA. For this purpose, flooding information are extracted from Sentinel 1 satellite imagery and fed into various supervised and unsupervised machine learning models, including Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood Classifier (MLC), to detect flooded pixels in images and evaluate the performance of these ML models. Moreover, a new unsupervised machine learning framework is developed which works based on the change detection (CD) approach and combines the Otsu algorithm, fuzzy rules, and iso-clustering methods for urban flood detection. Results from the performance evaluation of RF, SVM, MLC and CD models show 0.53, 0.85, 0.75 and 0.81 precision measures, 0.9, 0.85, 0.85 and 0.9 for recall values, 0.67, 0.85, 0.79 and 0.85 for the F1-score, and 0.69, 0.87, 0.83 and 0.87 for the accuracy measure, respectively, for each model. In conclusion, the new unsupervised flood image classification and detection method offers better performance with the least required data and computational time for enhanced rapid flood mapping. This systematic approach will be potentially useful for other cities at risk of urban flooding, and hopefully for detecting nuisance floods, by using satellite images and reducing the flood risk of transportation design and urban infrastructure planning.

Optimization of Embedded Sensor Packaging Used in Rollpave Pavement Based on Test and Simulation.

Rollpave pavement, as a rollable prefabricated asphalt pavement technology, can effectively reduce the overall road closure time required for pavement construction and maintenance. Sensors can be integrated into Rollpave pavement, thereby avoiding sensor damage that may otherwise result from high temperatures and compactive forces during the rolling process, as well as pavement structural damage resulting from cutting and drilling. However, the embedment of sensors into Rollpave pavement still presents certain challenges, namely poor interfacial synergy between the embedded sensor and the asphalt mixture. To solve this problem, three-point bending tests and dynamic response FEM simulations were used to optimize the embedded sensor’s packaging. The influence of sensor embedment on Rollpave pavement under different working conditions was analyzed. Results of these analyses show that low temperature and the epoxy resin negatively affect the bending performance of specimens, and that packaging with cylindrical shape, flat design, and consisting of a material with modulus similar to that of the asphalt mixture should be preferred. This study is conducive to improve the intellectual level and service life of road infrastructure.

Assessing Flood Indirect Impacts on Road Transport Networks Applying Mesoscopic Traffic Modelling: The Case Study of Santarém, Portugal

The key aspect for the quantification of indirect impacts of flooding is the assessment of the disruption of the transportation service considering social and economic consequences. To investigate how flooding can affect road transportation, it is essential to analyze interaction during the flood event itself, as well as on the following days. In this work, two static and dynamic traffic models are applied to a study zone for quantification of the performance and functionality of the network during the flood and after the failure of infrastructure components. A mesoscopic simulation was applied to identify the traffic disruption in the face of flood events. This simulation is capable of considering the road network model, assigning trip paths with the impact of road closures and speed reductions, and evaluating travel time and vehicle volume redistribution in a given disruption scenario. By comparing the traffic analysis results (travel time, travelled distance and street speed changes) in normal and flooded situations, the impact of flooding on a transportation network could be examined. Moreover, modelling outputs from a case study in the Santarém region (Portugal) indicated that in analyzing the flood impacts on a traffic network, even non-flooded infrastructures must be taken into account because of their service disruption.

Automated Vehicles Industry Survey of Transportation Infrastructure Needs

Automated vehicle (AV) deployment can bring about transformational changes to transportation and society as a whole. The infrastructure owner-operators (IOOs), who own, maintain, and operate the infrastructure, have the opportunity to work jointly with the AV industry to provide safe and efficient operations. A key question for the IOOs is, “What transportation infrastructure improvements do AV manufacturers believe will facilitate and improve AV performance?” This study was designed to address this question through a comprehensive survey approach, including an online survey and follow-up interviews. A list of ten questions was discussed, covering the physical and digital infrastructure, infrastructure maintenance, standards and specifications, policy support, data sharing, and so forth. The researchers reached out to more than 60 entities who hold the AV testing permit in California. In total, 20 companies responded. They were from different sectors and well represented the AV industry. From the results of this study, it is concluded that the most important roadway characteristics that have the potential to benefit the automated driving system (ADS) are: (1) digital mapping and signage; (2) lane markings; (3) work zone and incident information; (4) vehicle-to-everything (V2X) communications; (5) actual traffic signals; (6) general signage; and (7) lighting. The digital features considered most critical to help accelerate ADS deployment include work zone and road closure information, traffic signal phase and timing, and traffic congestion. This study provides diverse voices and in-depth insights into topics that the AV industry and IOOs should engage in to advance AVs’ deployment.

Characteristics of Infrastructure and Surrounding Geo-Environmental Circumstances Involved in Fatal Incidents Caused by Flash Flooding: Evidence from Greece

Recent studies have shown that a large portion of flood-associated fatalities are related to the use of vehicles. However, there is limited research on the role of infrastructure and geo-environmental conditions surrounding such incidents. This work exploits a dataset of 40 fatal vehicle-related events caused by flooding in Greece to explore the circumstances, the characteristics of infrastructure and of the surrounding environment and their possible influence in the studied incidents. The results showed that the majority of cases occurred in road segments and river crossings with a variety of design deficiencies related to safety and protection from flooding, including poor signage and lighting as well as the absence of road closures, road barriers and other crucial safety features. In high percentages incidents occurred in isolated/remote sites in rural areas, relatively narrow roads and low quality infrastructure (e.g., ford crossings) with a lack of easy alternate routes and absence of other individuals. In the vast majority of cases, the accidents occurred in small arid catchments drained by ephemeral torrents when experiencing extreme flows. The findings have practical implications as they provide a better understanding of dangerous circumstances and define the high-risk segments of the road, which is helpful in prioritizing and shaping appropriate interventions.

Rainfall Triggering of Post-Fire Debris Flows over a 28-Year Period near El Portal, California, USA

ABSTRACT Wildfires frequently affect the steep hillslopes near El Portal, California (United States), a small community established during the California Gold Rush in the mid-1800s. In addition to the historical significance of El Portal, State Route 140 (SR 140) is a major transportation and economic corridor connecting the San Joaquin Valley to Yosemite National Park (YNP). In 2019, an estimated 4.5 million tourists visited and accessed YNP via SR 140. In the years after wildfires, the burned watersheds produced debris flows during intense rainfall, impacting the El Portal community and motorists traveling on SR 140 and local roads. The steepness of the hillslopes and confinement of the valley limit options for mitigating debris-flow risk. As such, emergency managers are left with evacuation orders or temporary road closures as the best options for risk reduction. The effectiveness of these options is highly dependent on establishing an accurate local rainfall intensity-duration threshold that officials can use to guide emergency response actions and timing. We present an overview of the rainfall conditions that initiated 12 post-fire debris-flow events near El Portal from 1991 to 2018 and objectively define rainfall intensity-duration thresholds from triggering rainfall rates. Our results highlight the modest rainfall rates that triggered debris flows in these steep watersheds, while radar data from more recent events (2012–2018) portray the spatial variability of intense rainfall in the area. Additional rainfall monitoring is needed to provide a robust rainfall threshold that will effectively mitigate risk for residents and motorists while minimizing the impact of road closures and evacuations.

Metaheuristic enabled modified hidden Markov model for traffic flow prediction

Traffic flow prediction is essential for traffic management, pollution reduction, and public safety, and is an important component of Intelligent Transportation Systems (ITS). Since most of the links are not outfitted with traffic sensors, extracting traffic flow data in real-time scenarios has proven to be challenging. Moreover, the parameters that affect traffic flows (such as accidents, road closures, and public events) are mostly unexpected, which makes traffic flow prediction a complex task. Hence, this paper plans to design an enhanced prediction model for traffic flow using a Modified Hidden Markov Model (MHMM). The input features subjected to prediction via MHMM are “Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Indicator (RSI), and Rate of Change (ROC)” respectively. In fact, the modification in HMM relies on the optimal tuning of state numbers using the Mean Fitness-oriented Dragonfly Algorithm (MF-DA). Finally, the betterment of implemented work is compared and proved over the conventional models.

Analysis of Route Choice During Planned and Unplanned Road Closures

The Federal Highway Administration (FHWA) Alternate Route Handbook proposes guidance to identify alternate routes during planned and unplanned road closures. A challenge with this process is the lack of traffic data available to decision-makers. High volume corridors experiencing unplanned closures can provide a rich case history by systematically collecting connected vehicle (CV) data during such incidents. CV data provide the ability to directly measure actual diversion routes and travel times during an ongoing or historical incident. This paper presents methodologies to systematically analyze diversion data to identify the most common alternate route choices and impacted interstate exits, valuable information for public safety and transportation agencies to evaluate the surrounding road network’s resiliency in accommodating diverting traffic. Agencies can use this information to proactively deploy resources (officers, signs, barricades) at critical locations during future closures. The scalability of this methodology is demonstrated by evaluating 12 additional cases to assess diversion rates found to be in the range of 58% to 93% for total closures exceeding five hours. The paper concludes by recommending agencies apply these methodologies to develop data-driven diversion strategies on critical routes coupled with real-time CV monitoring in dispatch centers to provide agile adjustment of resources along diversion routes.