Road Segments Research Articles

Estimating urban noise along road network from street view imagery

Estimating road traffic noise is essential for examining the quality of sounding environment and mitigating such a non-negligible pollutant in urban areas. However, existing estimated models often have limited applicability to specific traffic conditions, while the required parameters may not be readily available for city-wide collection. This paper proposes a data-driven approach for measuring road-level acoustic information of traffic with street view imagery. Specifically, we utilize portable vehicle-equipped hardware for in-situ noise acquisition and employ a deep learning model ResNet to learn high-level visual features from street view images that are closely associated with road traffic noise. The ResNet captures meaningful patterns from the input data, and the output probability vectors are then fed into a Random-Forest regression algorithm to quantitatively estimate the noise in decibels for different road segments. The MAE and RMSE of the DCNN-RF model are 2.01 and 2.71, respectively. Additionally, we employ a gradient-weighted Class Active Mapping approach to visually interpret our deep learning model and explore the significant elements in streetscapes that contribute to the model's estimations. Our proposed framework facilitates low-cost and fine-scale road traffic noise estimations and sheds light on how auditory information could be inferred from street imagery, which may benefit practices in geography and urban planning.

Multi-Task Weakly Supervised Learning for Origin–Destination Travel Time Estimation

Travel time estimation from GPS trips is of great importance to order duration, ridesharing, taxi dispatching, etc. However, the dense trajectory is not always available due to the limitation of data privacy and acquisition, while the origin-destination (OD) type of data, such as NYC taxi data, NYC bike data, and Capital Bikeshare data, is more accessible. To address this issue, this paper starts to estimate the OD trips travel time combined with the road network. Subsequently, a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ulti-task <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">W</u> eakly <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> upervised <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</u> earning Framework for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ravel <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> ime <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</u> stimation (MWSL-TTE) has been proposed to infer transition probability between roads segments, and the travel time on road segments and intersection simultaneously. Technically, given an OD pair, the transition probability intends to recover the most possible route. And then, the output of travel time is equal to the summation of all segments' and intersections' travel time in this route. A novel route recovery function has been proposed to iteratively maximize the current routes' co-occurrence probability, and minimize the discrepancy between routes' probability distribution and the inverse distribution of routes' estimation loss. Moreover, the expected log-likelihood function based on a weakly-supervised framework has been deployed in optimizing the travel time from road segments and intersections concurrently. We conduct experiments on a wide range of real-world taxi datasets in Xi'an and Chengdu and demonstrate our method's effectiveness on route recovery and travel time estimation.

Crashes involving distracted pedestrians: Identifying risk factors and their relationships to pedestrian severity levels and distraction modes

The concept of distracted pedestrians and its impact on highway safety has gained increasing attention in recent years. However, studies focusing exclusively on distracted pedestrian crashes are less pervasive than distracted driving. In addition, most prior studies investigate the harmful effect of cellphone usage while walking, without considering other forms of pedestrian distraction. Also, the existing literature provides limited knowledge on comprehending the affinities between pedestrian distraction and safety consequences. This study aims to reveal the chain of contributing factors involved in distracted pedestrian crashes, considering both pedestrian severity levels and specific distraction-related tasks. Ten years (2010–2019) of related crashes were extracted from the Louisiana Department of Transportation and Development (LADOTD) database, and association rule mining (ARM) was applied to identify the meaningful crash patterns. Different distracting activities of pedestrians were introduced from the narratives of police-investigated crash reports. The study findings exhibit the complex nature of distracted pedestrian crashes by highlighting the intricate relationships between risk factors. On road segments, distracted male pedestrians aged 41–64 were more likely to be fatal/severely injured in dark-not-lighted conditions. Crashes involving pedestrians using electronic devices were often found at intersections. Distractions caused by pets, persons, or objects were strongly associated with crossing segments in rural settings. In-person conversation while standing on roadways in urban residential locations without traffic controls was found to increase vulnerability. Working on vehicles while wearing dark clothes and in dark-not-lighted conditions was identified as an influential factor in crash occurrence. Moreover, careless or inattentive actions of pedestrians while playing on the road segments were associated with a high likelihood of crashes. These study outcomes are crucial in uncovering the coexisting crash characteristics related to distracted pedestrians, which can be helpful in targeting and developing effective educational, design, and enforcement strategies to improve pedestrian safety.

GMAT-DU: Traffic Anomaly Prediction With Fine Spatiotemporal Granularity in Sparse Data

The fine-grained prediction of traffic anomalies is crucial for Traffic Management Bureau to alleviate congestion and avoid public safety incidents. While in practice, the fine-grained prediction is very challenging due to two issues. 1) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data sparsity</i> . At the fine-grained setting, missing data is inevitable and widespread on spatial and temporal dimension. Existing methods have weak performance as they do not handle missing data properly. 2) <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Data distribution mutation</i> . At the fine-grained setting, the traffic conditions of adjacent road segments are sometimes completely different, invalidating existing spatiotemporal smoothing-based methods. This paper proposes GMAT-DU, a novel model that aims to predict traffic anomaly from sparse data in fine-grained manner. To solve the first issue, we propose a Decay Unrolling (DU) mechanism to make the model applicable to sparse datasets. The performance will be progressively enhanced by the spatiotemporal unrolling of high-impact neighbors. For the second issue, we combine the meta-features of roads with correlations between roads, which are learnt from road semantic information and historical spatiotemporal data, and make the model focusing on the high-impact neighbors by a Graph Meta-features based ATtention (GMAT) mechanism. Extensive experiments on two real-world datasets validate the effectiveness of our method. The experiment results show the significant advantages against the state-of-the-art models.

Analysis of the Effects of Highway Geometric Design Features on the Frequency of Truck-Involved Rear-End Crashes Using the Random Effect Zero-Inflated Negative Binomial Regression Model

Statistical data indicate that trucks are more prone to rear-end crashes, making this an area of concern. The objective of this study is to create a model that analyzes the factors influencing the frequency of rear-end crashes involving trucks (TIRC). To achieve this, researchers identified the most appropriate model as Spatial Zero-Inflated Negative Binomial Regression (SZINB). This model takes into account spatial correlation, which plays a significant role in the occurrences of TIRC on different road segments supervised by each highway ward. The estimation of parameters in the SZINB model has led to key findings that shed light on the factors contributing to a higher likelihood of TIRC. These findings include the increased probability of TIRC on curved roads compared to straight ones, roads that feature open middle islands, six lanes per direction, a slope, right-of-way shoulder width, pavement type, lane width, and a post speed limit. Based on these key findings, this study developed policy recommendations and sample measures aimed at reducing the frequency of TIRC. Implementing measures such as improving the road design on curved sections, optimizing middle islands, and enhancing traffic management on wider roads can help mitigate the risk of crashes involving trucks.

An Embedding-Driven Multi-Hop Spatio-Temporal Attention Network for Traffic Prediction

Traffic prediction is an important part of modern intelligent transportation systems (ITS), which helps transportation management and city planning. However, it is a very challenging task for modeling complex spatio-temporal dependencies, since the traffic data belongs to highly periodic multivariate time series which makes it hard to model accurate spatial dependencies only from time series and observed geolocation information of road segments. The existing research mainly focuses on finding ways of capturing dynamic spatial dependencies of road segments while neglecting the importance of periodicity, and few studies have explored a pure embedding-driven method that is robust to corrupted data to model periodicity. In this paper, we propose an embedding-driven multi-hop spatio-temporal attention network for traffic prediction (), which mainly focuses on leveraging the multi-scale periodicity of traffic data. Specifically, the proposed network applies a designed Fourier-series-based embedding, to capture the periodicity, which is more in line with real-world facts. Driven by the designed embedding, both local and global temporal dependencies are modeled properly by combining the attention-based methods and the convolution-based methods. Besides, we implement a trial that can hardly be seen in the existing traffic prediction works to combine the graph self-attention mechanism with a multi-hop diffusion process to explore the large-scale structural information on a designed set of graphs. Experiments on two real-world traffic datasets which contains traffic speed data for months show the effectiveness of our proposed methods. The experiments also suggest the methods can provide stable reasonable and smooth predictions for completely corrupted data.

The Effect of Crowdsourced Police Enforcement Data on Traffic Speed: A Case Study of The Netherlands

The proliferation of smartphones and internet connectivity has provided the opportunity to use crowdsourced data in traffic management. Nowadays, many people use navigation apps such as Google Maps, Waze, and Flitsmeister to obtain real-time travel information and provide feedback on road conditions, such as reporting police speed checks. As an accurate traffic speed prediction is of great significance for road users and traffic managers, different models have been proposed and widely used to predict traffic speed considering the spatio-temporal dependence of traffic data and external factors such as the weather, accidents and points of interest. This study investigates the impact of crowdsourced data about police enforcement from navigation apps on traffic speed. In addition, we examine whether the police enforcement report affects the accuracy of the deep learning prediction model. The authors extract crowdsourced police enforcement information from navigation apps, collect the corresponding historical traffic speed data, and predict traffic speed in several corridors in The Netherlands using a GCN-GRU traffic speed prediction model. The results show that the crowdsourced data for police enforcement cause the average vehicle speed to drop between 1 [km/h] and 3 [km/h] when passing the road segments marked with police activity. Moreover, the prediction performance of the GCN-GRU model during the periods without police enforcement is better than the periods with reported police activity, showing that police speed check reports can decrease the accuracy of speed prediction models.

Pengaruh Gerak U-Turn pada Bukaan Median terhadap Karakteristik Arus Lalu Lintas

Transportation problems in big cities in Indonesia, especially on Jalan R. Suprapto, Ketapang Regency are increasing from time to time in line with population growth, rapid growth in the number of vehicles and vehicle ownership, urbanization and an inefficient public transportation system. This has an impact on the decrease in the performance level of the road segment, including the U-Turn motion behavior at the road median opening. This study aims to determine traffic characteristics, road capacity, road service level (LOS), the average time for vehicles to perform U-turns, queue lengths and alternatives that will be applied in this study based on the 1997 MKJI guidelines. From the results of research and discussion on Jalan R. Suprapto the characteristic value of traffic flow which consists of traffic volume at rush hour from the direction of Jl. R. Suprapto – Jl. Gatot Subroto at 513.8 smp/hour and for the direction of Jl. Gatot Subroto – Jl. R. Suprapto at 651.35 smp/hour. The vehicle speed is 49.86 km/hour. Traffic density for the direction of Jl. R. Suprapto – Jl. Gatot Subroto of 10.24 smp/km and for the direction of Jl. Gatot Subroto – Jl. R. Suprapto 13.1 pcu/km. The road segment capacity is 5,422.3 pcu/hour. The value of the road service level obtained an A value

Extraction of Roads Using the Archimedes Tuning Process with the Quantum Dilated Convolutional Neural Network.

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials, and geometries across different regions, pose a formidable obstacle for road extraction from RS imagery. The issue of road extraction can be defined as a task that involves capturing contextual and complex elements while also preserving boundary information and producing high-resolution road segmentation maps for RS data. The objective of the proposed Archimedes tuning process quantum dilated convolutional neural network for road Extraction (ATP-QDCNNRE) technology is to tackle the aforementioned issues by enhancing the efficacy of image segmentation outcomes that exploit remote sensing imagery, coupled with Archimedes optimization algorithm methods (AOA). The findings of this study demonstrate the enhanced road-extraction capabilities achieved by the ATP-QDCNNRE method when used with remote sensing imagery. The ATP-QDCNNRE method employs DL and a hyperparameter tuning process to generate high-resolution road segmentation maps. The basis of this approach lies in the QDCNN model, which incorporates quantum computing (QC) concepts and dilated convolutions to enhance the network's ability to capture both local and global contextual information. Dilated convolutions also enhance the receptive field while maintaining spatial resolution, allowing fine road features to be extracted. ATP-based hyperparameter modifications improve QDCNNRE road extraction. To evaluate the effectiveness of the ATP-QDCNNRE system, benchmark databases are used to assess its simulation results. The experimental results show that ATP-QDCNNRE performed with an intersection over union (IoU) of 75.28%, mean intersection over union (MIoU) of 95.19%, F1 of 90.85%, precision of 87.54%, and recall of 94.41% in the Massachusetts road dataset. These findings demonstrate the superior efficiency of this technique compared to more recent methods.

Comparison of NO2 and BC Predictions Estimated Using Google Street View-Based and Conventional European-Wide LUR Models in Copenhagen, Denmark

A widely used method for estimating fine scale long-term spatial variation in air pollution, especially for epidemiology studies, is land use regression (LUR) modeling using fixed off-road monitors. More recently, LUR models have been developed using data from mobile monitors that repeatedly measure road pollutants and mixed-effects modeling. Here, nitrogen dioxide (NO2) and black carbon (BC) predictions from two independent models were compared across streets (defined as 30–60 m road segments) (N = 30,312) and residences (N = 76,752) in Copenhagen, Denmark. The first model was Google Street View (GSV)-based mixed-effects LUR models (Google-MM) that predicted 2019 mean NO2 and BC levels, and the second was European-wide (EUW) LUR models that predicted annual mean 2010 levels at 100 m spatial resolution. Across street segments, the Spearman correlation coefficient between the 2019 NO2 from Google-MM-LUR and 2010 NO2 from EUW-LUR was 0.66, while at residences, this was 0.60. For BC, these were 0.51 across street segments and 0.40 at the residential level. The ratio of percentile 97.5 to 2.5 for NO2 across the study area streets using Google-MM NO2 was 4.5, while using EUW-LUR, this was 2.1. These NO2 ratios at residences were 3.1 using Google-MM LUR, and 1.7 using EUW-LUR. Such ratios for BC across street segments were 3.4 using Google-MM LUR and 2.3 using EUW-LUR, while at the residential level, they were 2.4 and 1.9, respectively. In conclusion, Google-MM-LUR NO2 for 2019 was moderately correlated with EUW-LUR NO2 developed in 2010 across Copenhagen street segments and residences. For BC, while Google-MM-LUR was moderately correlated with EUW-LUR across Copenhagen streets, the correlation was lower at the residential level. Overall, Google-MM-LUR revealed larger spatial contrasts than EUW-LUR.

Tinjauan Kerusakan dan Analisis Ulang Tebal Perkerasan Menggunakan PCI dan Analisa Komponen

This study analyzes the road conditions in Sumbawa Regency and plans pavement solutions to address road damage. The data shows that there was no significant improvement in road conditions in 2020 compared to previous years. Good and fair road conditions only accounted for 69.29% of the total road length, while damaged and heavily damaged roads accounted for 6.57% and 24.14% respectively. Jalan Osapsio is one of the roads that has been damaged due to poorly maintained drainage. This research utilizes the Pavement Condition Index (PCI) method to assess the level of road damage and the Component Analysis method to design the pavement thickness. Based on the findings, several road segments exhibit different conditions. There are 13 segments with a Good rating and PCI values of 100 and 98, 1 segment with a Serious rating and a PCI value of 16, and 6 segments with a Failed rating and PCI values of 0. The segments with a Failed rating require urgent pavement reconstruction. Considering the PCI values, it is recommended to construct new pavement with appropriate thickness for the priority segments, namely a 6 cm thick Laston Surface Layer, a 20 cm thick Broken Stone Upper Base Layer, and a 15 cm thick Sirtu/Pitrum Lower Base Layer.This study provides an important contribution in understanding the road conditions in Sumbawa Regency and offers concrete solutions to address the road damage on Jalan Osapsio. The findings and research methodology are expected to serve as a reference for immediate improvements on Jalan Osapsio and provide insights for future research in Sumbawa Regency.

Mapping of Carbon Dioxide (Co2) Emissions Due To Increased Vehicle Volume in Mataram City

The high level of population and activities in the city of Mataram also increases the use of vehicles as a secondary need for the community to help with daily activities in land transportation. As the use of vehicles increases, the demand for fuel oil (BBM) also increases. Fuel is a material that can be converted into energy by burning fuel where the fuel contains heat energy that can be released and manipulated. Vehicle emissions are a major source of pollution in big cities in Indonesia. CO2 is a compound consisting of the elements carbon and oxygen with the writing CO2. Carbon dioxide is a gaseous element that exists at atmospheric temperature and pressure. The hope for this research is to determine CO2 levels in the city of Mataram and prevent CO2 emissions from increasing. The results of calculating the highest vehicle volume are located in segment III Jalan Majapahit of 3506 pcu/hour which occurs at 17:00 - 17:15 WITA. The results of the regression analysis between vehicle volume as the X variable and CO2 gas emissions as the Y variable on the five road segments conducted by the study were y = 134.57 ln(x) - 970.14 with an R2 value of 0.8661 included in the 0.80 interval – 1,000 which means that the strength of the relationship between vehicle volume and CO2 emissions has a very strong relationship and vehicle volume contributes 86.6165% to CO2 emissions. Based on the results of the discussion that was carried out, the CO2 gas emission levels in all road segments carried out had an average CO2 gas emission level value of 103.31 – 108.56 kg/hour.

Toward safer highway work zones: An empirical analysis of crash risks using improved safety potential field and machine learning techniques

Due to complex traffic conditions, transition areas in highway work zones are associated with a higher crash risk than other highway areas. Understanding risk-contributing features in transition areas is essential for ensuring traffic safety on highways. However, conventional surrogate safety measures (SSMs) are quite limited in identifying the crash risk in transition areas due to the complex traffic environment. To this end, this study proposes an improved safety potential field, named the Work-Zone Crash Risk Field (WCRF). The WCRF force can be used to measure the crash risk of individual vehicles that enter a work zone considering the influence of multiple features, upon which the overall crash risk of the road segment in a specific time window can be estimated. With the overall crash risk used as a label, the time-window-based traffic data are used to train and validate an eXtreme Gradient Boosting (XGBoost) classifier, and the Shapley Additive Explanations (SHAP) method is integrated with the XGBoost classifier to identify the key risk-contributing traffic features. To assess the proposed approach, a case study is conducted using real-time vehicle trajectory data collected in two work zones along a highway in China. The results demonstrate that the WCRF-based SSM outperforms conventional SSMs in identifying crash risks in work zone transition areas on highways. In addition, we perform lane-based analysis regarding the impact of setting up work zones on highway safety and investigate the heterogeneity in risk-contributing features across different work zones. Several interesting findings from the analysis are reported in this paper. Compared to existing SSMs, the WCRF-based SSM offers a more practical and comprehensive way to describe the crash risk in work zones. The approach using the developed WCRF technique offers improved capabilities in identifying key risk-contributing features, which is expected to facilitate the development of safety management strategies for work zones.

A global empirical study on how street networks facilitate driving longer distances

We simulated over 200 cities worldwide to investigate how the street network affects vehicle routes. We demonstrate that there is a ubiquitous super-linear relationship between time and distance when optimal route are chosen. More precisely, the average speed will be higher for longer trips when compared to shorter trips, showing that the street network makes driving further faster. We attribute this phenomenon to the spatial arrangement of extensive street segments that eliminate deceleration points. These results underscore the importance for cities to consider the distribution of deceleration-free streets while mitigating any negative impact on sustainability. To ensure efficient transportation planning and engineering, innovative approaches are necessary to facilitate the flow of goods and services while adhering to sustainable mobility principles.

Mobile monitoring of air pollutants; performance evaluation of a mixed-model land use regression framework in relation to the number of drive days.

We used black carbon data from a mobile monitoring campaign in Oakland, USA measuring street segments up to 40 times and compared a data-only, LUR model and mixed-model approach with a long-term average, represented by the average concentration based on 40 drive days on that street segment. The mixed model outperformed the data-only and LUR model estimates, with 80% explained variance after 5 drive days and 90% after 14 drive days. The data-only approach needed 8 and 15 to achieve an explained variance of 80% and 90%, respectively, The LUR model never achieved an explained variance higher than 70%. The mixed model is a scalable approach, as it can be used before all street segments in a domain are measured by developing a LUR model and adds information with increasing repeats per street segment.

Walking as soft mobility: A multi-criteria GIS-based approach for prioritizing tourist routes

Walking, as the main mode of soft mobility, has become an integral aspect of urban tourism. The development of pedestrian tourist routes plays a crucial role in enhancing the positive walking experience in urban tourist destinations. This research employs a hybrid multi-criteria decision-making (MCDM) with GIS to rank street segments, establishing walking-oriented tourist paths in Shiraz's historic district, Iran. The initial steps involve identifying pivotal criteria through literature reviews and expert surveys. The Analytical Hierarchy Process (AHP) establishes criteria and sub-criteria weights using input from 30 experts. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) then prioritizes street segments based on sub-criteria such as traffic safety, image ability and heritage assets, security, land use and activity, accessibility, comfort and convenience, and human scale. Furthermore, a Space Syntax analysis (SSA) is conducted to evaluate the morphology of the street network in the study area and identify streets with potential for pedestrian movement. By integrating these analyses, a prime corridor for a pedestrian tourist route is identified. This study offers planners and policymakers a valuable tool for pre-investment decision-making, aiding the promotion of walkability in tourist-centric areas.

Measuring urban quality and change through the detection of physical attributes of decay

The quality of the urban environment is crucial for societal well-being. Yet, measuring and tracking the quality of urban environment, their evolution, and spatial disparities is difficult due to the amount of on-the-ground data needed to capture these patterns. The growing availability of street view images presents new prospects in identifying urban features. However, the reliability and consistency of these methods across different locations and time remains largely unexplored. We aim to develop a comprehensive index of urban quality and change at the street segment level using Google Street View (GSV) imagery. We focus on eight object classes that indicate urban decay or contribute to an unsightly urban space, such as potholes, graffiti, garbage, tents, barred or broken windows, discolored or dilapidated façades, weeds, and utility markings. We train an object detection model on a dataset of images from different cities and assess the performance of these urban indices. We evaluate the effectiveness of this method in various urban contexts over time and discuss its potential for urban planning and public policy. We demonstrate the use of these indices in three applications: the Tenderloin in San Francisco, the Doctores and Historic Center neighborhoods in Mexico City, and South Bend, Indiana.

Exploring bus tracking data to characterize urban traffic congestion

Quantification of traffic dynamics is a valuable tool for city planning and management. Metrics such as the vehicle average speed, travel time, delays, and count of stops, can be used to characterize mobility and traffic congestion in an area. However, effective study of mobility data is often hindered by the difficulty of gathering mobility data in a practical, inexpensive, and prompt way.In this work, we explore the use of city buses as mobility probes, using the existing smart city infrastructure deployed in Aveiro, Portugal. We propose a method for traffic congestion detection considering the low vehicle speed, low traffic flow and road occupancy close to its capacity. Three degrees of congestion are identified using the k-means approach; DBSCAN is used to characterize the typical level of congestion in a road. Using four-weeks of mobility data, it was possible to assess the congestion along the day and for the different days of the week; some road segments proved to be consistently prone to congestion. We also studied parameters of driving safety, considering speed and acceleration.In this work, we show that knowledge discovery can be applied to mobility data being collected by tracking buses, exploring data that is often collected for other purposes also to characterize traffic congestion. These methods can inform decision makers and are easily ported to other cities.

A data-driven traffic shockwave speed detection approach based on vehicle trajectories data

Traffic shockwaves demonstrate the formation and spreading of traffic fluctuation on roads. Existing methods mainly detect the shockwaves and their propagation by estimating traffic density and flow, which presents weaknesses in applications when traffic data is only partially or locally collected. This paper proposed a four-step data-driven approach that integrates machine learning with the traffic features to detect shockwaves and estimate their propagation speeds only using partial vehicle trajectory data. Specifically, we first denoise the speed data derived from trajectory data by the Fast Fourier Transform (FFT) to mitigate the effect of spontaneous random speed fluctuation. Next, we identify trajectory curves’ turning points where a vehicle runs into a shockwave and its speed presents a high standard deviation within a short interval. Furthermore, the Density-based Spatial Clustering of Applications with Noise algorithm (DBSCAN) combined with traffic flow features is adopted to split the turning points into different clusters, each corresponding to a shockwave with constant speed. Last, the one-norm distance regression method is used to estimate the propagation speed of detected shockwaves. The proposed framework was applied to the field data collected from the I-80 and US-101 freeway by the Next Generation Simulation (NGSIM) program. The results show that this four-step data-driven method could efficiently detect the shockwaves and their propagation speeds without estimating the traffic densities and flows nearby. It performs well for both homogenous and nonhomogeneous road segments with trajectory data collected from total or partial traffic flow.

Developing operating speed models for elevated multilane urban arterials using artificial neural networks

Operating speed models help assess and evaluate geometric design consistency along successive road segments. The development of operating speed models has been mostly focused on rural two-lane two-way highways, where horizontal curvature plays a dominant role in speed prediction. The need to enhance the prediction power of operating speed models and ability to capture more complex relationships within an urban setting have motivated this investigation. This research investigates the use of artificial neural networks, to develop operating speed models for multilane urban elevated arterial roads. Variables investigated in this study included geometric/operational features of a road segment in addition to the residual impact of the characteristics of upstream segments. A data collection exercise was undertaken on two major urban elevated arterial roads in Greater Cairo Region, Egypt: 6th of October and Saft Al-Laban corridors. Speed data was extracted from Google Distance Matrix Application Programming Interface and validated using test vehicle speed data. A regression-based modeling exercise was undertaken in the preliminary investigation phase to serve as a benchmark for the intended machine learning modeling exercise. Results showed that the prediction power of the developed ANN models — capturing the residual effect of upstream speeds — outperformed regression-based ones. The best-performing model used operating speeds of two upstream segments in addition to geometric/operational features of the segment under investigation to predict the segment operating speed (the model reported MAPE of 6.7%). Outputs of this model were used in a design consistency evaluation and potential transferability exercises to further investigate the model practicality.