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Road networks facilitate socioeconomic growth in developing countries, such as the Philippines. An increased traffic volume exacerbates the gradual progression of erosion, breaching, and degradation of road embankments. Weathered marble from local quarries in Norzagaray, Bulacan, was repurposed to provide an economic solution. This study quantified the effect of five varying proportions of weathered marble and aggregate on index properties, such as specific gravity, grain size distribution, plasticity, and compaction, and assessed the capacity of the embankment against vehicular loads using the California bearing ratio and direct shear tests. A mix of 60% marble and 40% aggregate (60M40A) was determined to display the best performance in terms of maximum dry density (20.512 kN/m3) and California bearing ratio (78%). The Proctor compaction test results indicated a very stable embankment according to the USCS criteria and good to excellent embankment performance according to the AASHTO criteria. The local criteria on embankment marked the 60M40A samples as an excellent material for the embankment base layer. The same specimen demonstrated the highest shear strength before failure and had the highest friction angle before failure (37 degrees). These results indicate that weathered marble exhibits excellent compaction behavior, yields friction angles comparable to well-graded soil, and possesses a suitable load-bearing capacity, thus establishing its feasibility as a potential material for road embankments, especially in rural areas within proximity to marble quarrying sites. [ABSTRACT FROM AUTHOR]

This paper analyzes the emergence of two well-defined peaks during the morning peak period in the traffic flow diurnal curve. It selects six California cities as research targets, and uses California employment and household travel survey data to explain how and why this phenomenon has risen during the pandemic. The final result explains that the double-humped phenomenon results from the change in the composition of commuters during the morning peak period after the outbreak. [ABSTRACT FROM AUTHOR]

This study aims to explore the relationship between traffic flow states and crash type/severity in the scenarios of normal crashes, primary crashes, and secondary crashes using the association rules mining approach. The crash data and real‐time traffic data were collected from the I‐880 freeway for five years in California, USA. The secondary crashes were identified using a speed contour plot approach. Traffic flow states were identified by the three‐phase flow theory. The results showed that the free flow is associated with the proportion of the sideswipe normal crash, the hit object primary crash, and the injury primary crash. The synchronized flow, the wide moving jams, and the transitional state from synchronized flow to wide moving jams are associated with the proportion of the rear‐end secondary crash. The transitional state from synchronized flow to free flow is associated with the proportion of the rear‐end primary crash and the property damage only primary crash. In addition, the unsafe speed behaviour can increase the proportion of the rear‐end normal, primary, and secondary crashes. The unsafe lane change behaviour can increase the proportion of the sideswipe normal, primary, and secondary crashes. These results have the potential to reduce the secondary crash probability. [ABSTRACT FROM AUTHOR]

A methodology of in-situ mixing of soil concrete in a quarry has been proposed to guarantee the required characteristics after implementation. The manufactured soil concrete is a mixture of granitic aggregates and lateritic gravels. Geotechnical tests were used to determine the physical and compaction characteristics of the studied materials. The results obtained show that the rocks encountered in the Gamboula area are granites with good to excellent impact resistance (LAA < 45%), good wear resistance (Micro Deval < 15%), and compaction characteristics (Maximum dry density MDD = 2.29 g/cm3, California bearing ratio CBR = 100%) suitable for use as a base course for T1 to T4 volume traffic. The nodular materials developed on these granitic rocks are thick (2.55 m) and present geotechnical characteristics required for use in sub-base (MDD = 2.09 g/cm3, CBR = 56%). The addition of 25% of 0/31.5 granite aggregates improves the characteristics of the lateritic gravels by decreasing the plasticity index, increasing the MDD, and obtaining a CBR value > 60%. A mixing logigram of the aggregate-soil mixture with the mechanical shovel was proposed. This logigram indicates that for these materials, mixing step number 5 gives a better homogeneity of the mixture and the best geotechnical characteristics, especially the CBR. After this step, the mixtures obtained can be implemented without risk of segregation or alteration of the geotechnical characteristics of the mixture. [ABSTRACT FROM AUTHOR]

Coordinated ramp metering (CRM) systems are implemented on freeways primarily to improve operational conditions. However, smoother traffic flow resulting from CRM may also have significant safety benefits. The main objective of this research is to evaluate the safety performance of CRM systems on the Interstate 80 (I-80) corridor in California by using an empirical Bayes before–after approach. We collected geometric features, traffic volume, and historical crash data from I-80 in the San Francisco Bay area (Caltrans District 4). Then, the freeway safety prediction methodology implemented using the Enhanced Interchange Safety Analysis Tool (ISATe, developed as part of a National Cooperative Highway Research Program Project 17-45) was utilized to estimate the counterfactual, i.e., the number of crashes if no CRM system was implemented on the corridor. Based on a comparison of this counterfactual with the actual crash counts in the post-CRM period, CRM implementation led to a decrease in the number of fatal and injury crashes on I-80 but an increase in minor property damage only crashes. Disaggregate analysis of the results was used to gain further understanding of the CRM safety performance. The differences in the resulting safety performances were contextualized based on the different settings where the systems were implemented. As expected, CRM systems were more effective for segments in the vicinity of ramps. Safety performance functions for shorter durations (e.g., for peak hours), the subject of the ongoing National Cooperative Highway Research Program Project 22-48, will help more precisely estimate the safety impact of CRMs. [ABSTRACT FROM AUTHOR]

The deployment of hydrogen refueling stations is vital to the development of fuel cell vehicles. Past studies on the hydrogen refueling stations didn't consider congestion on paths and assumed all flows are on the shortest paths of OD pairs, which is not consistent with travelers' route choice behavior. In this paper, we study flow refueling location allocation based on the user-equilibrium (UE) status, which is consistent with travelers' route choice behavior. We present a flow-capturing location allocation (FCLA) model on UE conditions. We also presented an equilibrium algorithm avoiding cannibalization to solve the model. Based on the transportation network of Anaheim city in California in the US, we compared the results of applying a non-equilibrium method to the FCLA model and applying the equilibrium method to the FCLA model on UE conditions. The results of the two algorithms and models are different. The equilibrium model and algorithm can capture more flow and has a higher flow-capturing ratio. What's more, the equilibrium model and algorithm can fully capture the expected flow in practice since the real transportation network is basically in UE status. The equilibrium model and algorithm presented in this paper is the first study of this kind to locate stations in cities and is useful in the deployment of hydrogen refueling stations. • Study flow refueling location allocation based on the user-equilibrium (UE) status. • A flow capturing location allocation (FCLA) model on UE conditions. • An equilibrium algorithm avoiding cannibalization to solve the model. • Compare between non-equilibrium method and equilibrium method in flow capturing. [ABSTRACT FROM AUTHOR]

Traffic congestion is ubiquitous in major cities around the world. Congestion is associated with a slew of negative effects, including delays and local air pollution. Because of the negative effects of congestion, governments invest billions of dollars into the highway system to try to reduce congestion and accommodate peak-hour automobile travel demand. The COVID-19 pandemic presented a significant disruption to transportation systems globally. One impact was a drastic reduction in travel, leading to free-flowing traffic conditions in many previously-congested cities. As lockdowns eased, traffic volumes returned to near-normal levels. However, the temporal pattern of demand may differ, due to increased remote work or other factors. In this article, we examine the temporal distribution of highway demand in California, using data from over 3,500 traffic sensors. We find that peak-hour automobile travel is spreading in the post-lockdown period. In addition to decreased traffic congestion, this finding also has implications for infrastructure investment. Roadways are generally sized based on peak-hour demand. As the peaks spread, some highway construction project may prove unnecessary. It may be possible to reallocate road space to other uses with fewer tradeoffs in terms of traffic congestion. [ABSTRACT FROM AUTHOR]

Single chip seals are used by many agencies to maintain or preserve their roadways. While the construction and performance of single chip seals can be easily found from literature, the construction of double chip seals with and without paving fabric or paving mats is still not common. This paper investigates four double chip seal strategies used in a pilot project constructed on US 395 in Inyo County, California, by Caltrans. Within the double chip seal project limits, eight Performance Evaluation Sections (PESs) using four treatment strategies were established for detailed performance monitoring and evaluation: 1—a 3/8-inch asphalt rubber chip seal followed by a 1/4-inch PME chip seal without pavement-reinforcing fabric (PRF) or a paving mat as a control section; 2—a 3/8-inch PME chip seal over PRF, followed by a 1/4-inch PME chip seal; 3—a 3/8-inch PME chip seal over a paving mat, followed by a 1/4-inch PME chip seal; and 4—an asphalt rubber 3/8-inch chip seal over a paving mat, followed by a 1/4-inch PME chip seal. This pilot project was monitored during construction and evaluated 1 year later to help identify any construction issues and was used to improve the specifications and performance of Caltrans' chip seals. This paper presents the initial findings following construction, and the one-year performance of the pilot project and lessons learned. The findings presented were accomplished by using these four treatment strategies on a highway with a very adverse high desert climate type and high traffic volumes. Project reviews are also planned for up to seven years to determine the long-term project performance. [ABSTRACT FROM AUTHOR]

Secondary crashes (SCs) are typically defined as the crash that occurs within the spatiotemporal boundaries of the impact area of the primary crashes (PCs), which will intensify traffic congestion and induce a series of road safety issues. Predicting and analyzing the time and distance gaps between the SCs and PCs will help to prevent the occurrence of SCs. In this paper, a combined data-driven method of static and dynamic approaches is applied to identify SCs. Then, the random forests (RF) method is implemented to predict the two gaps using temporal, primary crash, roadway, and real-time traffic characteristics data collected from 2016 to 2019 at California interstate freeways. Subsequently, the SHapley Additive explanation (SHAP) approach is employed to interpret the RF outputs. The results show that the traffic volume, speed, lighting, and population are considered the most significant factors in both gaps. Furthermore, the main and interaction effects of factors are also quantified. High volume possibly promotes the time and distance gaps, while low volume inhibits them. And volume affects the distance gap inconsiderably when it falls between 300 and 400 veh/5 min. Traffic conditions with high speed and low volume are strongly associated with short-time and short-distance gaps. Darker surroundings probably accelerate the occurrence of SCs. Moreover, crashes involving the violation categories of improper turns or unsafe lane changes likely result in long time and distance gaps. These results have important implications for proposing traffic management and improving road safety. [ABSTRACT FROM AUTHOR]

Accurate forecasting of traffic flow is crucial for intelligent traffic control and guidance. It is very challenging to forecast the traffic flow due to the high non‐linearity, complexity and dynamicity of the data. Most existing forecasting methods focus on designing complicated graph neural network architectures to capture the spatio‐temporal features of traffic data with the help of predefined graphs. However, traffic data exhibit a strong spatial dependency, which means that there are often complex correlations between nodes in a road network topology graph. Moreover, the spatial correlations of the road network change over time. To capture the properties of the road network topology graph, a novel spatio‐temporal adaptive graph convolutional networks model (STAGCN) based on deep learning is proposed. Utilize adaptive graph generation block to capture the static and dynamic structures of the traffic road network respectively, and they are integrated to construct an adaptive road network topology graph. Then the spatio‐temporal features of the traffic data are captured using spatio‐temporal convolution blocks. Experiments were conducted on publicly available traffic datasets for freeway traffic in California, USA, and the results showed that the prediction accuracy of the STAGCN model outperformed multiple baseline methods. [ABSTRACT FROM AUTHOR]

Intelligent traffic control and guidance system is an effective way to solve urban traffic congestion, improve road capacity and guarantee drivers' travel safety, while short-term traffic flow prediction is the core of intelligent traffic control and guidance system. To investigate the long-term memory and the dynamic feature of short-time traffic flow time series, a hybrid model was proposed by integrating autoregressive fractionally integrated moving average (ARFIMA) model and nonlinear autoregressive (NAR) neural network model to predict short-time traffic flow, in which ARFIMA model can address the long-term memory of linear component and NAR neural network can accommodate the dynamic feature of nonlinear residual component. First, the ARFIMA model was employed to predict the linear component of traffic flow, and the results were compared with those of autoregressive integrated moving average (ARIMA) model. Next, the NAR neural network model was adopted to forecast the nonlinear residual components, and the weighted results were considered as the predicted flow of the hybrid model. The proposed hybrid model was validated by using the cross-sectional traffic flow data in California freeways obtained from the open-access PeMS database. The results showed that the ARFIMA model considering the long-term memory can effectively predict the short-term traffic flow, and the prediction accuracy of the hybrid model is better than that of the singular models. The findings provide an alternative for the short-term traffic flow prediction with lower error and higher accuracy. [ABSTRACT FROM AUTHOR]

Traffic flow prediction has been essential for traffic management and road network planning. However, the complex urban road network and the strong spatialtemporal correlation of traffic flow data make this problem difficult. Existing prediction methods cannot fully utilize the spatial-temporal correlations in traffic flow data. Therefore, we propose a deep learning model called ResGAT-ABiGRU which combines Residual Network (ResNet), Graph Attention Network (GAT), Attention Mechanism, and the Bidirectional Gated Recurrent Unit (BiGRU). Firstly, GAT is used to capture the spatial correlations of traffic flow data, and then the time characteristics are extracted by Bidirectional GRU. Secondly, the ResNet module stacks multiple GAT layers and designs the attention mechanism to assign weights for different flow sequences to further capture spatial relations. Finally, we obtain the output through the fully connected layers. Validation traffic data from California, USA, is used for verification. The results show that the ResGAT-ABiGRU model proposed in this paper has higher prediction accuracy. Compared the model's performance with the Gated Recurrent Unit (GRU) baseline model, and the root means square error (RMSE) is reduced by 22.75%, and compared to the T-gcn model, the root mean square error is reduced by 3.29%. [ABSTRACT FROM AUTHOR]

Understanding the causal impact of meteorological variations on traffic conditions (e.g., traffic flow and speed) is crucial for effective traffic prediction and management, as well as the mitigation of adverse weather effects on traffic. However, many existing studies focused on establishing associations between meteorological situations and traffic, rather than delving into causal relationships, especially with deep learning techniques. Consequently, the ability to identify specific meteorological conditions that significantly contribute to traffic congestion or delays is still limited. To address this issue, this study proposes the M eteorological- T raffic C ausal I nference V ariational A uto- E ncoder Model (MT-CIVAE) to estimate the causal impact of fine-grained meteorological variations (e.g., rain and temperature) on traffic. Specifically, MT-CIVAE is based on the Variational Auto-Encoder and consists of an encoder to recover the distribution of latent confounders and a decoder to estimate the conditional probabilities of treatments. Transformer encoder layers are incorporated to analyze the spatial and temporal correlations of historical traffic data to further enhance the inference capability. To evaluate the effectiveness of the proposed approach for causal inference, real-world traffic flow and speed datasets collected from California, along with corresponding fine-grained meteorological datasets, are employed. The counterfactual analysis is conducted using artificially generated meteorological conditions as treatments, which allows for the simulation of hypothetical meteorological scenarios and the evaluation of their potential impact on traffic conditions. This study develops deep learning methods for assessing the causal impact of meteorological variations on traffic dynamics, offering explanations and insights that can assist transportation institutions in guiding post-meteorology traffic management strategies. • Develop causality-aware deep methods between meteorological variations and traffic dynamics. • The designed MT-CIVAE effectively captures complex spatiotemporal correlations. • The designed MT-CIVAE effectively mitigates influence of undetermined confounders. • Experiments have been conducted on real-world traffic flow and speed data from California. [ABSTRACT FROM AUTHOR]

A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions. [ABSTRACT FROM AUTHOR]

Automated vehicle (AV) technology is expected to make roads safer. However, until recently only a handful of studies could test such hypotheses due to limited access to testing data. This study contributes to the literature by jointly analyzing the associated factors of three interrelated outcome variables--vehicle at fault, collision type, and injury outcome in AV-involved crashes. We use Bayesian networks to analyze the manually extracted data from reports of 333 AV-involved crashes that occurred in California between January 2017 and October 2021. The summary statistics indicate that rear-end collisions are the dominant (63.5%), while AVs are at fault for a small proportion of crashes (14.4%), and a majority of crashes (84.4%) are noninjury. The joint inferences of the Bayesian networks show that irrespective of the collision type, when the AV is at fault, the chance of the physical injury in a crash increases significantly. Further, the chance of an AV being at fault seems higher in parking locations, and during driving at wet pavements in unclear weather. The chances of AV rear-end collisions are lower in the parking lot, and when nonvehicular participants are involved but increase in high traffic flow roadways. We also find that the likelihood of physical injury is higher at high-speed locations, intersections, and wet pavements. These insights suggest specific areas (unsignalized intersections, less structured right-of-way rules, and wet pavements) where technological improvements could enhance the safety performance of AVs. [ABSTRACT FROM AUTHOR]

Despite their promise, popularity, and rapid growth, the transit implications of ride-hailing platforms (e.g., Uber, Lyft) are not altogether clear. On the one hand, ride-hailing services can provide pooling (i.e., traffic reductions) advantages by efficiently matching customer demand (i.e., trips) with resources (i.e., cars) or by facilitating car-sharing. On the other hand, ride-hailing may also induce extra travel because of increased convenience and travel mode substitution, which may create crowding (i.e., traffic increases). We seek to reconcile these divergent perspectives here, exploring the heterogeneous determinants of ride-hailing's effects. Taking advantage of Uber's staggered entry into various geographic markets in California, we execute a regression-based difference-in-differences analysis to estimate the impact of ride-hailing services on traffic volumes. Using monthly micro data from more than 9,000 vehicle detector station units deployed across California, we show that Uber's effect (either pooling or crowding) on traffic depends on various contextual factors. We find some evidence of pooling effects on weekdays; however, Uber's entry leads to significant crowding effects on weekends. Furthermore, the crowding effect is amplified on interior roads and in areas characterized by high population density. Although ride-hailing seems to have a substitution effect on public transportation, we find ride-hailing services may have a complementary effect for carpooling users. Finally, we show that premium ride-hailing services (e.g., Uber Black) almost exclusively lead to a crowding effect. We conduct a battery of robustness tests (e.g., propensity score matching, alternative treatment approaches, placebo tests) to ensure the consistency of our findings. [ABSTRACT FROM AUTHOR]

Short‐term forecasting of traffic flow is an indispensable part of easing traffic pressure. Considering that different traffic flow patterns will affect the short‐term traffic flow prediction results, a combined method based on the K‐means clustering algorithm and gated recurrent unit (GRU) is proposed to build a short‐term traffic flow prediction model to overcome the above problems. The K‐means algorithm is used to cluster historical traffic flow data to establish different traffic flow pattern libraries. The K‐nearest neighbour (KNN) classification algorithm is used to determine the historical traffic flow pattern most similar to the traffic flow change trend of the date to be predicted. All historical traffic flow data in this category is used training samples to make targeted predictions. The traffic flow data of performance measurement system (PeMS) in California, USA is used to verify the performance of the proposed model. Compared with the GRU network, stacked auto encoders (SAEs), random forest (RF), and support vector machine regression (SVR), the results show that the proposed combination model K‐means‐GRU considers the diversity of traffic flow patterns and improves the prediction accuracy, it can better solve the short‐term traffic flow prediction problem. [ABSTRACT FROM AUTHOR]

An accurate and reliable forecast for traffic flow is regarded as one of the foundational functions in an intelligent transportation system. In this paper, a new model for traffic flow forecasting, named EnGS-DGR, is designed based on ensemble learning of graph convolutional network (GCN), sequence-to-sequence (Seq2Seq) learning model, and dynamic graph reconfiguration (DGR) algorithm. At the first stage, instead of employing entire nodes in the traffic network, the DGR algorithm is proposed to reconstruct the traffic graph topology consisting of traffic nodes with tight correlation under a specific forecasting interval, where the degree of correlation among the traffic nodes is quantized from the perspective of multi-view clustering. At the second stage, GCN-Seq2Seq integration strategy is introduced to extract the data of the spatio-temporal dependence and forecast traffic flow. We applied the proposed EnGS-DGR to two different datasets from the highways of Los Angeles County and of California's Bay Area; the simulation results show that the proposed EnGS-DGR is superior to other eight popular models for traffic flow forecasting in terms of three common performance metrics. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting, which requires modelling involuted spatial and temporal dependence and uncertainty regarding road networks and traffic conditions, is a challenge for intelligent transportation systems (ITS). Recent studies have mainly focused on modelling spatial-temporal dependence through a fixed weighted graph based on prior knowledge. However, collecting up-to-date and accurate road information is costly. Moreover, is a single fixed graph enough to describe the correlation between sensors? The fixed weighted graph cannot directly relate to prediction tasks, which may result in considerable biases. To tackle this issue, in this paper, we propose a novel deep learning model framework: an adaptive spatial-temporal graph attention network (ASTGAT). Our ASTGAT simultaneously learns the dynamic graph structure and spatial-temporal dependency for traffic flow forecasting. Specifically, our framework consists of two joint training parts: a Network Generator model that generates a discrete graph with the Gumbel-Softmax technique and a Spatial-Temporal model that utilizes the generated network to predict traffic speed. Our Network Generator can adaptively infer the hidden correlations from data. Moreover, we propose a graph talking-heads attention layer (GTHA) for capturing spatial dependencies and design a gate temporal convolution (GTCN) layer for handling long temporal sequences. We evaluated our ASTGAT on two public datasets: METR-LA is collected in Los Angeles and PEMS-BAY is collected in California. Experimental results indicate that our ASTGAT outperforms the state-of-the-art (SOTA) baselines. Finally, to further describe our model, we visualize the forecasting results and the generated graph. [ABSTRACT FROM AUTHOR]

Compared to normal incidents, secondary incidents are more likely to result in severe injuries and fatalities. However, limited efforts have been made to unveil the factors affecting the severity of secondary incidents. Incidents that occurred on the Interstate-5 in California within five years were collected. Detailed dynamic traffic flow conditions, geometric characteristics and weather conditions were obtained. First, a Random Forest-based (RF) feature selection approach was adopted. Then, Support Vector Machine (SVM) models were developed to investigate the effects of contributing factors. For comparison, RF and Ordered Logistic (OL) models were also built based on the same dataset. It was found that the SVM model has high capacity for solving classification problems with limited data availability. Further, sensitivity analysis assessed the impacts of explanatory variables on the injury severity level. Explanatory variables, including occupancy, duration, frequency of lanes changes, and number of lanes, were found to contribute to injury severity of secondary incidents. Occupancy difference between upstream and downstream and duration are the most significant factors of high injuries in secondary incidents. Smoothing these traffic conditions after an incident occurs and responding fast in incident handing and clearance both have potential to reduce road trauma caused by secondary incidents. [ABSTRACT FROM AUTHOR]

Objective: Automated Truck-Mounted Attenuators (ATMAs) have the potential to improve work zone safety by removing the human driver out of a vehicle that is positioned in work zones to absorb impact from errant vehicles. However, this automated technology is expensive and can be detrimental to safety and project success if operated incorrectly (e.g., operating limitations and procedures not followed). Therefore, it is important to understand users' perceptions of ATMAs and how training can improve appropriate adoption of this technology. The objective of this study was to evaluate how work zone workers perceive the usefulness of and the capabilities of automation in Truck-Mounted Attenuators.Methods: A survey study was conducted with 13 Department of Transportation (DOT) workers in Colorado and California. Each of the DOT workers in this study had some previous experience with the ATMA, either in real-world applications and/or formal training. The survey collected information on participant job specifications, experience with the ATMA, training received, trust in the ATMA, usability of the HMIs, and operating capabilities of the automation.Results: Workers reported an overall positive acceptance of this technology. This was supported by their expectation that it would reduce crash severity; that there was a reasonable workload associated with operating procedures for the automation; and by their overall trust in the automation's reliability. However, workers noted concerns regarding their trust in the automation under various contexts, such as poor visibility and denser traffic volumes. Further, trust in the technology was greatest among workers with higher levels of ATMA training and longer experience working with the ATMA.Conclusions: This research presents a novel perspective on user acceptance of ATMA technology. These findings can help jurisdictions achieve the safety improvements that investment and deployment of automation in work zones offers, by identifying the disconnect between operators and technology. [ABSTRACT FROM AUTHOR]

Detecting high-collision-concentration locations based solely on collision frequency may produce different results compared to those considering the severities of the collisions. In particular, it can lead government agencies focusing sites with a high collision frequency while neglecting those with a lower collision frequency but a higher percentage of injury and fatal collisions. This study developed systematic ways of detecting reproducible fatal collision locations (R) using the naïve Bayes approach and a continuous risk profile (CRP) that estimates the true collision risk by filtering out random noise in the data. The posterior probability of fatal collisions being reproducible at a location is estimated by the relationship between the spatial distribution of fatal-collision locations (i.e., likelihood) and the CRP (i.e., prior probability). The proposed method can be used to detect sites with the highest proxy measure of the posterior probability (PMP) of observing R. An empirical evaluation using 5-year traffic collision data from six routes in California shows that detecting R based on the PMP outperform those based on the SPF-based approaches or random selection, regardless of various conditions and parameters of the proposed method. This method only requires traffic collision and annual traffic volume data to estimate PMP that prioritize sites being R and the PMPs can be compared across multiple routes. Therefore, it helps government agencies prioritizing sites of multiple routes where the number of fatal collisions can be reduced, thus help them to save lives with limited resources of data collection. [ABSTRACT FROM AUTHOR]

The objective of this study is to analyse the relationship between secondary crash risk and traffic flow states and explore the contributing factors of secondary crashes in different traffic flow states. Crash data and traffic data were collected on the I-880 freeway in California from 2006 to 2011. The traffic flow states are categorised by three-phase traffic theory. The Bayesian conditional logit model has been established to analyse the statistical relationship between the secondary crash probability and various traffic flow states. The results showed that free flow (F) state has the best safety performance of secondary crash and synchronized flow (S) state has the worst safety performance of secondary crashes. The traditional logistic regression model has been used to analyse the contributing factors of secondary crashes in different traffic flow states. The results indicated that the contributing factors in different traffic flow states are significantly different. [ABSTRACT FROM AUTHOR]

Highway bridge load rating has been moving toward structural reliability since the issuance of AASHTO LRFR specifications; however, the recommended load factors were carried out by a few reliable truck data. The objective of this study is to calibrate the live load factor in AASHTO LRFR Rating Specification by using huge amount of WIM data collected in California for more than ten years between 2001 and 2013. Since traffic volumes, vehicular overloads, and traffic components are highly related to the load effect induced, a set of calibration equations is proposed here, in which the nominal standard load effect models are used and different requirements of loading are taken into account. By the analytical model of platoons of trucks and the extrapolation of the gathered WIM data over a short period of time to remote future over a longer time period, the expected maximum live load effects over the rating period of 5 years are also obtained. Then, the live load factor is calibrated as the product of the codified value multiplied by the ratio between the nominal standard load effect and the expected mean value. The results show that the products of the two ratios present rather constant, implying the proposed method and load configurations selected are effective. In the end, the live load factors of 1.0 and 0.7 along with load configurations are recommended for a simple span length less than 300 ft. The recommended calibration method and live load factors will eliminate the unnecessary overconservatism in rating specifications. [ABSTRACT FROM AUTHOR]

Cooperative adaptive cruise control (CACC) vehicles need vehicle-to-vehicle (V2 V) communication to achieve CACC function. When a CACC vehicle follows a manual-driven vehicle (MDV) without V2 V communication, it needs degenerate to adaptive cruise control (ACC). By using real experiments, California PATH program indicated that ACC vehicles are apt to be unstable, which may have negative influence on fuel consumption and traffic emissions. Hence, this paper studies the impacts of the mixed CACC-MDV traffic on fuel consumption and emissions, by taking into consideration partial degenerations from stable CACC vehicles to unstable ACC vehicles. To deal with this, microscopic simulations were adopted by using car-following models. Then, an appropriate emission model was used for evaluating the emission impacts under different CACC market penetration rates (MPRs). In order to obtain reliable evaluation results, the models validated by PATH program using real experimental data were employed as the CACC and ACC car-following models. In addition, we also analytically investigated stability of the mixed traffic flow under different CACC MPRs, in order to explore its relationship with the emission impacts. The results show that the fuel consumption and emissions firstly increase and then decrease with the increase of the CACC MPR. This means the mixed traffic under some ranges of CACC MPRs will produce more fuel consumption and emissions, compared with the full MDVs traffic. It indicates that stability situations of the mixed traffic qualitatively influence the impact trend of CACC MPRs on fuel consumption and emissions. Then, V2 V communication equipments on MDVs are not only encouraging but also essential to avoid the deterioration of fuel consumption and emissions of the mixed traffic flow. From a novel perspective, emission impacts of mixed CACC-HDVs flow are studied by considering partial degeneration from stable CACC to unstable ACC. It showed that stability of the mixed traffic flow qualitatively influences the impact trend of emissions with respect to CACC market penetration rates (MPR). Full V2 V communication environment is essential to reduce fuel consumption and emissions in the mixed traffic flow. [ABSTRACT FROM AUTHOR]

Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures. [ABSTRACT FROM AUTHOR]

Prediction of the driver-vehicle-unit (DVU) future state is a challenging problem due to many dynamic factors influencing driver capability, performance and behavior. In this study, a soft computing method is proposed to predict the accelerating behavior of driver-vehicle-unit in the genuine traffic stream that is collected on the California urban roads by US Federal Highway Administration's NGSIM. This method is used to predict DVU velocity for different time-steps ahead using adaptive neuro-fuzzy inference system (ANFIS) predicator. To evaluate the performance of proposed method, standard time series forecasting approach called autoregressive (AR) model is considered as a rival method. The predictions accuracy of two methods are compared using root mean square error (RMSE), mean absolute percentage error (MAPE) and coefficient of determination or R-squared (R2) as three error criteria. The results demonstrate the adequacy of proposed algorithm on real traffic information and the predicted speed profile shows that ANFIS is able to predict the dynamic traffic changes. The proposed model can be employed in intelligent transportation systems (ITS), collision prevention systems (CPS) and etc. [ABSTRACT FROM AUTHOR]

The occurrence of secondary accidents leads to traffic congestion and road safety issues. Secondary accident prevention has become a major consideration in traffic incident management. This paper investigates the location and time of a potential secondary accident after the occurrence of an initial traffic accident. With accident data and traffic loop data collected over three years from California interstate freeways, a shock wave-based method was introduced to identify secondary accidents. A linear regression model and two machine learning algorithms, including a back-propagation neural network (BPNN) and a least squares support vector machine (LSSVM), were implemented to explore the distance and time gap between the initial and secondary accidents using inputs of crash severity, violation category, weather condition, tow away, road surface condition, lighting, parties involved, traffic volume, duration, and shock wave speed generated by the primary accident. From the results, the linear regression model was inadequate in describing the effect of most variables and its goodness-of-fit and accuracy in prediction was relatively poor. In the training programs, the BPNN and LSSVM demonstrated adequate goodness-of-fit, though the BPNN was superior with a higher CORR and lower MSE. The BPNN model also outperformed the LSSVM in time prediction, while both failed to provide adequate distance prediction. Therefore, the BPNN model could be used to forecast the time gap between initial and secondary accidents, which could be used by decision makers and incident management agencies to prevent or reduce secondary collisions. [ABSTRACT FROM AUTHOR]

• The study investigated the transferability of real-time safety models for signalized intersections to new jurisdictions. • The real-time safety models relate rear-end conflicts at through lanes to various dynamic traffic parameters. • Two corridors in California and Atlanta States were used in the analysis as destination jurisdictions. • The models' transferability was evaluated with and without a local calibration at the new jurisdictions. • The results showed that the models are transferable and can be used for real-time safety optimization. Optimizing traffic signals in real-time for safety performance can be executable in the era of Connected Vehicles (CVs) when real-time information on vehicle positions and trajectories is available. To achieve this, real-time safety models are needed to understand how changes in signal controllers affect safety in real-time. Recently, several real-time safety models were developed for signalized intersections that relate various dynamic traffic parameters to the number of rear-end traffic conflicts at the signal cycle level. The traffic parameters included: traffic volume, maximum queue length, shock wave speed and area, and platoon ratio. For wider application of these models to other jurisdictions, the transferability of these models needs to be examined. Therefore, this paper aims to investigate the transferability of several signalized intersections real-time safety models to new jurisdictions. Two corridors of signalized intersections in California and Atlanta were used in the analysis as destination jurisdictions. Detailed vehicle trajectories for these corridors were obtained from the Next Generation Simulation (NGSIM) data. Various transferability analysis approaches were applied. The transferability of the real-time safety models was evaluated with and without a local calibration for the model parameters at the new jurisdictions. Several goodness-of-fit measures were examined to assess the ability of the developed models to predict traffic conflicts. Overall, the results showed that the real-time safety models are transferable, which confirms the validity of using them for real-time safety evaluation of signalized intersections. [ABSTRACT FROM AUTHOR]

This study aims to compare and quantify the impact of traffic volume on hotspot identification. The data consist of geometric and traffic features of freeways of California for the six-year period (2005-2010). Five functional roadway classifications were used for analyzing the role of traffic volume in different environments. Four hotspot identification (HSID) methods were selected for this study, namely, Empirical Bayesian count with volume (EBWT), Empirical Bayesian count without volume (EBWOT), crash rate (CR), and crash number (CF). To determine the superiority of the above methods, four evaluation tests were conducted which include Site Consistency Test (SCT), Method Consistency Test (MCT), Total Rank Difference Test (TRDT), and Total Performance Difference Test (TPDT). The safety performance functions (SPF) that include the traffic volume show a better fit to crash count than do the ones without traffic volume. The results also show that EBWT mostly comes out to be the superior method as indicated by the four tests, followed by EBWOT, CF, and the worst performer CR. The advantages associated with the inclusion of traffic volume in SPFs are also transferred to the HSID with EBWT showing the best performance in most cases. [ABSTRACT FROM AUTHOR]

• This paper studies the severity of freeway primary-secondary crash pairs' severity. • It integrates data from different data sources. • It develops a copula-based model to simultaneously account for the the correlation among primary-secondary crash pairs' severity. • It reveals similarities and differences in the factors that significantly influence primary and corresponding secondary crashes severity. A copula-based model is developed in this study to jointly model the severity of freeway primary crashes and secondary crashes. The copula-based model can concurrently account for the severity levels in the crash and the correlation among primary-secondary crash pairs' severity. The model comprehensively considers a series of explanation variables, including temporal characteristics, crash characteristics, roadway characteristics and real-traffic conditions, and is estimated using traffic crash data from 2016 through 2019 for Los Angeles County, California. The proposed copula model is then contrasted with the traditional binary probit model and the results show a remarkable advantage of the copula model, which is evidenced by better fitting performance. It is found that weather, whether towed away, unsafe speed, collision type, road condition, terrain, road weaving and truck involvement have significant impact on primary crash severity propensity and collision type, road width, road condition, traffic volume and vehicle speed have significant impact on secondary crash severity propensity. In light of the findings, a number of countermeasures are proposed to mitigate freeway crashes, including emergency services, vehicle and roadway engineering, traffic law enforcement and driver education. [ABSTRACT FROM AUTHOR]

This study investigated the trends of ambient ozone (O 3) levels and their associations with precursor gases and meteorology using ground and satellite observations in California during the peak O 3 seasons of 2005-2017. The decrease in ground-level nitrogen dioxide (NO 2) concentrations due to air pollution control strategies improved the O 3 air quality by 0.67 ppb/year on average, while local meteorology worsened the O 3 air quality by 0.11 ppb/year. The improvement of O 3 air quality that was attributed to NO 2 was more pronounced on higher O 3 air pollution days with the largest improvement for the 90th percentile of O 3 (0.85 ppb/year). The O 3 trends were modified more by the reductions of NO 2 (a proxy of nitrogen oxides (NO x)) than those of non-methane organic compounds (NMOC; a proxy of volatile organic compounds (VOCs)). The highest and lowest contributions of NO 2 relative to NMOC to the reduction of O 3 were found in the Sacramento Valley and South Coast, respectively. Satellite Ozone Monitoring Instrument (OMI) formaldehyde (HCHO) to NO 2 ratios, an indicator of O 3 sensitivity to precursor gases, were the lowest in the South Coast (specifically the corridor connecting downtown Los Angeles and Riverside), suggesting the highest potential of O 3 to be influenced by VOCs. In addition, the OMI HCHO/NO 2 ratios increased over time across California (0.23 per year), which demonstrated that the O 3 air quality was increasingly sensitive to NO x. The satellite-based finding was consistent with ground NO 2 -NMOC-O 3 associations concerning the direction and relative extent of NO 2 and NMOC contributions to the O 3 trends. Therefore, the NO x emission controls are expected to continue mitigating O 3 levels and protect public health from adverse health outcomes associated with O 3 and also NO 2. Nonetheless, the O 3 air quality may further benefit from local-scale strategies for VOC controls in certain localized areas (e.g., high traffic volumes). • Regulatory actions to control NO 2 emissions improved ambient O 3 air quality. • The NO 2 -driven O 3 declines were more pronounced on higher O 3 air pollution days. • Ambient O 3 trends were modified more by the reductions of NO 2 than by those of NMOC. • Satellite OMI HCHO/NO 2 reasonably inferred ground-level O 3 sensitivity. • The OMI HCHO/NO 2 approach was useful to evaluate and guide O 3 regulatory programs. [ABSTRACT FROM AUTHOR]

The article explores the presence of Los Angeles, California freeway travel and traffic in the literary and film world. Particular focus is given to the emergence of Century Freeway as an oasis in the 1994 film "Speed." Topics discussed include vehicular traffic in Bret Easton Ellis' novel "Imperial Bedrooms" and traffic as the ultimate theatre for the dramatisation of truth in J. G. Ballard's novel "Crash."

Background Areas near parks may present active travelers with higher risks than in other areas due to the confluence of more pedestrians and bicyclists, younger travelers, and the potential for increased traffic volumes. These risks may be amplified in low-income and minority neighborhoods due to generally higher rates of active travel or lack of safety infrastructure. This paper examines active travel crashes near parks and builds on existing research around disparities in park access and extends research from the Safe Routes to School and Safe Routes to Transit movements to parks. Methods We utilized the Green Visions Parks coverage, encompassing Los Angeles County and several other cities in the LA Metropolitan area. We used negative bionomial regression modeling techniques and ten years of geolocated pedestrian and bicyclist crash data to assess the number of active travel injuries within a quarter mile (~400 m) buffer around parks. We controlled for differential exposures to active travel using travel survey data and Bayesian smoothing models. Results Of 1,311,736 parties involved in 608,530 crashes, there were 896,359 injuries and 7317 fatalities. The number of active travel crash injuries is higher within a quarter-mile of a park, with a ratio of 1.52 per 100,000 residents, compared to areas outside that buffer. This higher rate near parks is amplified in neighborhoods with high proportions of minority and low-income residents. Higher traffic levels are highly predictive of active travel crash injuries. Conclusions Planners should consider the higher risks of active travel near parks and the socioeconomic modification of these risks. Additional traffic calming and safety infrastructure may be needed to provide safe routes to parks. [ABSTRACT FROM AUTHOR]

• Effectiveness of autonomous braking systems was tested by simulation. • Three safety strategies were developed for Automatic Preventive Braking system. • Proposed Automatic Preventive Braking is safer than Autonomous Emergency Braking. • Proposed Automatic Preventive Braking is not overly conservative. • Jerk-bounded braking profile can improve driving comfort. In public road tests of autonomous vehicles in California, rear-end crashes have been the most common type of crash. Collision avoidance systems, such as autonomous emergency braking (AEB), have provided an effective way for autonomous vehicles to avoid collisions with the lead vehicle, but to avert false alarms, AEB tends to apply late and hard brake only if a collision becomes unavoidable. Automatic preventive braking (APB) is a new collision avoidance method used in Mobileye's Responsibility-Sensitive Safety (RSS) model that aims to reduce crashes with a milder brake and decreased impact on traffic flow, but APB's safety performance is inferior to that of AEB. This study therefore proposes three safety improvement strategies for APB, the addition of response time, safety buffer, and minimum following distance; and combines them in different ways into four improved APB systems, IP1-IP4. Simulating car-following safety–critical events (SCEs) extracted from the Shanghai Naturalistic Driving Study in MATLAB's Simulink, the safety performance, conservativeness, and driving comfort of the four systems were evaluated and compared with the original APB system, two AEB systems, and human drivers. The results show that 1) IP4, the system that integrated all three strategies, outperformed the baseline APB and IP1-IP3 and prevented all SCEs from becoming crashes; 2) IP4 was slightly more conservative than AEB, but less conservative than RSS; 3) APB's jerk-bounded braking profile improved driving comfort; and 4) higher deceleration was found in the two AEB systems (both 8.1 m/s2) than in IP4 (6.7 m/s2), but they failed to prevent all crashes. Our proposed APB system, IP4, can provide safe, efficient, and comfortable braking for AVs in car-following SCEs, and has the potential to be practically applied in vehicle collision avoidance systems. [ABSTRACT FROM AUTHOR]

Traffic-related air pollution is a serious problem with significant health impacts in both urban and suburban environments. Despite an increased realization of the negative impacts of air pollution, assessing individuals' exposure to traffic-related air pollution remains a challenge. Obtaining high-resolution estimates are difficult due to the spatial and temporal variability of emissions, the dependence on local atmospheric conditions, and the lack of monitoring infrastructure. This presents a significant hurdle to identifying pollution concentration hot spots and understanding the emission sources responsible for these hot spots, which in turn makes it difficult to reduce the uncertainty of health risk estimates for communities and to develop policies that mitigate these risks. We present a novel air pollution estimation method that models the highway traffic state, highway traffic-induced air pollution emissions, and pollution dispersion, and describe a prototype implementation for the San Francisco Bay Area. Our model is based on the availability of real-time traffic estimates on highways, which we obtain using a traffic dynamics model and an estimation algorithm that augments real-time data from both fixed sensors and probe vehicles. These traffic estimates combined with local weather conditions are used as inputs to an emission model that estimates pollutant levels for multiple gases and particulates in real-time. Finally, a dispersion model is used to assess the spread of these pollutants away from the highway source. Maps generated using the output of the dispersion model allow users to easily analyze the evolution of individual pollutants over time, and provides transportation engineers and public health officials with valuable information that can be used to minimize health risks. [ABSTRACT FROM AUTHOR]

Traffic incidents due to non-recurring congestion frequently occur in urban environments. In this study, we propose the estimation and detection of traffic incidents based on independent component analysis (ICA) and hybrid observer (HO)-generalized likelihood ratio (GLR) techniques. First, we develop the traffic time series to obtain insight into the traffic flow and to detect traffic incidents. Then, we use time series analysis to construct complex networks. Next, we propose the ICA technique to monitor traffic flow. Then, we introduce a piecewise switched linear model based observer to estimate the possible occurrence of traffic incidents. Finally, we propose a new incident detection method that combines HO and GLR techniques. The combined HO-GLR method can produce better incident detection, improve traffic safety, and enhance traffic management systems. We have validated the effectiveness of the proposed method using simulated traffic data generated from the Ayer Rajah Expressway in Singapore and a real-world dataset from the I-880 freeway of California. The performance metrics used to evaluate the performance of the proposed method includes detection rate, false alarm rate, classification rate, mean time to detection and the area under receiving operating characteristics curve. The experimental results show that the proposed method has obtained better performance in all of the criteria when compared with other well-known methods. [ABSTRACT FROM AUTHOR]

Good urban design is essential if transit stations are to successfully connect to the communities that surround them. Increasingly, transit agencies in the US are constructing light rail systems in and above freeway medians to reduce land acquisition costs, minimize traffic conflicts and increase train speeds. Elevated stations, however, are difficult to physically link to surrounding communities, resulting in lost opportunities for transit-oriented development (TOD). This study examines all 14 elevated and freeway-median light rail stations along the Green and Gold lines in Los Angeles and documents the challenges of integrating them to the surrounding urban fabric. It suggests remedies based on a review of good urban design practices and interventions for different contexts that have been successful in improving station access. The study tests some of these interventions using as case studies four elevated Green Line stations. [ABSTRACT FROM PUBLISHER]

Three alternative construction plans with full highway closures and limited capacity closures (partial closures) are evaluated for a large-scale freeway reconstruction project in the Sacramento metropolitan area. By using a dynamic network analysis tool, the potential impact resulting from different construction plans is estimated and analyzed measured by changes in total demand, travel delays, vehicle miles/hours traveled (VMT/VHT), fuel consumptions, and emissions. The results show that, for this Interstate 5 closure, if only user delay costs are considered, the actual full closure plan is the best. It produces considerably less total system travel delay than the two partial closure plans although it closed more lanes. This plan, however, may induce more VMT than the other two. In fact, the other two partial closure plans produce slightly fewer emissions and consume slightly less fuel. The outcome of which plan comes out on top crucially depends on two other factors, besides the chosen objective: the amount of demand reduction a plan induces and the availability of alternative routes to divert traffic away from the work zone area. Developing efficient demand management measures will be the key to improving the network performance and to reduce the emissions. Moreover, when data are available, construction costs and emissions produced by construction activities should also be included in the evaluations, in addition to the user costs considered in this study. [ABSTRACT FROM AUTHOR]

Abstract: On–road particulate matter (PM) was collected during a sampling campaign in March-April of 2011 on two major Los Angeles freeways, I-710 and Route 110. I-710 is a major route for heavy-duty vehicles (HDVs) traveling to and from the Ports of Long Beach and Los Angeles, while Route 110 has a much lower HDV fraction –3.9% versus 11.4%. Two sets of samples were collected for each roadway, each set representing approximately 50°h of on-road sampling. Concurrent sampling at a fixed site at the University of Southern California''s (USC) downtown Los Angeles campus provided estimates of urban background levels. Chemical analysis was performed for elemental carbon (EC), organic carbon (OC), polycyclic aromatic hydrocarbons (PAHs), hopanes and steranes, and metals and trace elements. Freeway-based emission rates (ERs) – mass per kilometer of freeway per hour – were calculated using mass concentrations, fuel characteristics, and traffic flow rates. These ERs are presented such that freeways could be treated as a line source of emissions for use in predictive models of population exposure for nearby communities. This data could also be used to assess the exposure of commuters to traffic-related PM2.5 emissions. ERs are compared to data from a previous fixed-site roadside study of I-710 as well as to reconstructed values from a tunnel study. ERs were generally lower (or comparable) on the gasoline-vehicle dominated freeway (Route 110) than the freeway with more diesel trucks (I-710), with EC and pyrene being notably lower on Route 110, findings consistent with the Route 110''s lower HDV fraction. We found EC emission rates decreased over time suggesting that efforts to reduce diesel emissions from HDVs at the Ports of Los Angeles and Long Beach have been successful. While ERs for most of the organic species were within the range of values reported by previous studies, the present study found much higher ERs for metals and trace elements. This suggests that the sampling methods employed in this campaign are more efficient at capturing particles from sources such as resuspended road dust and wear from tires and brakes, which are usually not included in traditional sampling methodologies for assessing vehicular emissions (e.g. dynamometer studies). [Copyright &y& Elsevier]

Heavy vehicles can considerably affect traffic flow particularly during heavy-traffic conditions. Large numbers of heavy-vehicle lane-changing maneuvers can contribute to increase the number of traffic accidents and hence to reduce the freeway safety. The increase in the number of heavy vehicles on freeways has been the motivation to establish strategies to reduce the interaction between heavy vehicles and passenger cars. Previous studies have examined different lane-restriction strategies for heavy vehicles using microscopic traffic-simulation packages. Those packages mostly use a general lane-changing model to estimate the lane-changing behavior of heavy-vehicle and passenger-car drivers. The general lane-changing models ignore the fundamental differences in the lane-changing behavior of passenger cars and heavy vehicles. However, an exclusive lane-changing model for heavy vehicles can increase the accuracy of simulation models. The application of such a model can result in a more reliable evaluation of lane restriction strategies. In this paper, different lane restriction strategies are defined for heavy vehicles. For each strategy, the macroscopic and microscopic traffic measurements of two freeway sections in California are analyzed, using the VISSIM default lane-changing model and an exclusive heavy- vehicle lane-changing model. The results show that the VISSIM default model unrealistically overestimates the observed number of heavy-vehicle lane-changing maneuvers and potentially overestimates the number of traffic accidents. Using the exclusive lane-changing model for heavy vehicles enhances the accuracy of the VISSIM traffic simulation model in microscopically estimating the lane-changing maneuvers of heavy vehicles. [ABSTRACT FROM AUTHOR]

Scatterplots of traffic speed versus flow have received considerable attention over the past decades due to their characteristic half-moon shape. Modelling data of this type is difficult as both variables are actually not a function of each other in the sense of causality, but are rather jointly generated by a third latent variable, which is a monotone function of the traffic density. We propose local principal curves (LPCs) as a tool to describe and model speed-flow data, which takes this viewpoint into account. We introduce the concept of calibration curves to determine the relationship between the latent variable (represented by the parametrisation of the principal curve) and the traffic density. We apply LPCs to a variety of speed-flow diagrams from Californian freeways, including some so far unreported patterns. [ABSTRACT FROM AUTHOR]

Abstract: Evaluation of Intelligent Transportation Systems (ITS) at the planning level requires the use of appropriate tools that can capture the dynamic and stochastic interactions between demand and supply. The objective of this paper is to present a methodological simulation-based framework for such applications and implement it in the context of dynamic traffic assignment. The framework consists of a mesoscopic supply simulator and a demand simulator that combines OD estimation capabilities with discrete travel behavior models. Simulation-based DTA systems are particularly suited to evaluate a wide range of Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS). The simulation model performance is illustrated through two large-scale case studies in Irvine, California, and Lower Westchester County, NY. [ABSTRACT FROM AUTHOR]

This paper analyzes urban multimodal transportation systems in an aggregated way. To describe the aggregate behavior of traffic in cities, use is made of an idea that is now receiving some attention: the macroscopic fundamental diagram (MFD). We demonstrate through simulation how the MFD can be used to monitor and control a real network, in this case a portion of San Francisco, using readily available input data. We then show how different modes interact on the same network and discuss how these interactions might be incorporated into an MFD for multimodal networks. The work unveils two main results: first, it confirms recent results showing that restricting access to a city's congested areas can improve mobility for all travelers, including those who endure the restrictions; and second, that dedicating street space to collective transport modes can improve accessibility for all modes, even those from which space is taken away. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, the reduction of freight vehicle trips during peak hours has been a common policy goal. To this end, policies have been implemented to shift logistics operations to nighttime hours. The purpose of such policies has generally been to mitigate congestion and environmental impacts. However, the atmospheric boundary layer is generally more stable during the night than the day. Consequently, shifting logistics operations to the night may increase 24-h average concentrations of diesel exhaust pollutants in many locations. This paper presents realistic scenarios for two California cities, which provide diesel exhaust concentration and human intake estimates after temporal redistributions of daily logistics operations. Estimates are made for multiple redistribution patterns, including from 07:00–19:00 to 19:00–07:00, similar to daytime congestion charging polices, and from 03:00–18:00 to 18:00–03:00, corresponding to the PierPASS program at the ports of Los Angeles and Long Beach. Results for these two redistribution scenarios indicate that 24-h average exhaust concentrations would increase at most locations in California, and daily human intake is likely to worsen or be unimproved at best. These results are shown to be worse for inland than coastal settings, due to differences in meteorology. Traffic congestion effects are considered, using a new graphical method, which depicts how off-peak policies can be environmentally improving or damaging, depending on traffic speeds and meteorology. [ABSTRACT FROM AUTHOR]

Abstract: This paper is concerned with the macroscopic merging behavior of traffic at fully congested freeway merges, where a queue is present at all (upstream and downstream) approaches. An existing theory states that this behavior is dictated by a fixed ratio between the two upstream merging flows, denoted as the merge-ratio. It has been further conjectured that the merge-ratio is equal to the capacity-ratio. This paper presents an effective method to estimate merge-ratios from extensive historical traffic data. The archived traffic data in the California PEMS (Performance Measurement System) from January 2004 to June 2008 are used to estimate merge-ratios at 15 different freeway-to-freeway merge sites (via connectors). Findings show that merge-ratios can be reasonably estimated by the ratios between the numbers of lanes on the merging approaches (lane-ratio), which is typically similar to the capacity-ratio. However, residual differences between merge-ratios and lane-ratios suggest that there are probably other influencing factors. [Copyright &y& Elsevier]

Abstract: Transportation network design is a method for analyzing the interactive benefits of transportation projects applied to a network. In this paper, a network design model is developed for long haul freight movements which are represented by relationships between shippers and carriers. Additionally, an explicit capacity constraint is used to divert traffic volume from congested links. A case study based on the California transportation network is implemented to examine the effectiveness of this model when applied to a large network. A geographic information system is used to facilitate data management and analysis of the results. [Copyright &y& Elsevier]

In 1996, California Department of Transportation (Caltrans) initiated an extensive project to develop mechanistic-empirical (ME) pavement design and analysis tools, which will help state design engineers incorporate the impact of new products and technologies, increased traffic volumes and axle loading, and variable climatic conditions. As part of the development of new ME design procedures, Caltrans is developing a suite of dedicated software. CalME is a ME design program for flexible pavements that parallels the NCHRP 1-37A (MEPDG) and is calibrated for California conditions. CalBack is a sophisticated back-calculation program that contains specific features pertinent to California and is designed to work standalone, or in concert with CalME. CalBack is a unique and important addition to the existing knowledge base in that it possesses multiple data input/output methods, several new analytical modeling methods, and numerous user performance options. This paper describes the successful development and implementation of CalBack in three ways: (1) why and how Caltrans developed CalBack; (2) how CalBack functions; and (3) representative studies that show how well the software performs. Application of CalBack in several California projects, including the case studies presented in this paper, has shown that back-calculated moduli for different layers in the flexible and rigid pavement systems agreed reasonably well with laboratory measured values. Furthermore, the importance of avoiding temperature gradients when evaluating portland cement concrete pavements with the falling weight deflectometer is also well captured by CalBack. [ABSTRACT FROM AUTHOR]