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Cellular vehicle-to-everything (C-V2X) is a communication technology that supports various safety, mobility, and environmental applications, given its higher reliability properties compared to other communication technologies. The performance of these C-V2X-enabled intelligent transportation system (ITS) applications is affected by the performance of the C-V2X communication technology (mainly packet loss). Similarly, the performance of the C-V2X communication is dependent on the vehicular traffic density which is affected by the traffic mobility patterns and vehicle routing strategies. Consequently, it is critical to develop a tool that can simulate, analyze, and evaluate the mutual interactions of the transportation and communication systems at the application level to quantify the benefits of C-V2X-enabled ITS applications realistically. In this paper, we demonstrate the benefits gained when using C-V2X Vehicle-to-Infrastructure (V2I) communication technology in an energy-efficient dynamic routing application. Specifically, we develop a Connected Energy-Efficient Dynamic Routing (C-EEDR) application using C-V2X as a communication medium in an integrated vehicular traffic and communication simulator (INTEGRATION). The results demonstrate that the C-EEDR application achieves fuel savings of up to 16.6% and 14.7% in the IDEAL and C-V2X communication cases, respectively, for a peak hour demand on the downtown Los Angeles network considering a 50% level of market penetration of connected vehicles. The results demonstrate that the fuel savings increase with increasing levels of market penetration at lower traffic demand levels (25% and 50% the peak demand). At higher traffic demand levels (75% and 100%), the fuel savings increase with increasing levels of market penetration with maximum benefits at a 50% market penetration rate. Although the communication system is affected by the high density of vehicles at the high traffic demand levels (75% and 100% the peak demand), the C-EEDR application manages to perform reliably, producing system-wide fuel consumption savings.The C-EEDR application achieves fuel savings of 15.2% and 11.7% for the IDEAL communication and 14% and 9% for the C-V2X communication at the 75% and 100% market penetration rates, respectively. Finally, the paper demonstrates that the C-V2X communication constraints only affect the performance of the C-EEDR application at the full demand level when the market penetration of the connected vehicles exceeds 25%. This degradation, however, is minimal (less than a 2.5% reduction in fuel savings). [ABSTRACT FROM AUTHOR]

With the continuous enrichment of traffic Internet-of-Things data acquisition methods, more and more spatiotemporal data on road networks is collected in real time by various sensors and multimedia devices. The data-driven deep learning approach can make full use of real-time data from a road network to predict future traffic status. By mining the spatiotemporal relationships between road units, the ability to predict network evolutionary behaviors is improved, which provides a new method of traffic management. There are strong semantic relations between road intersections or road sections in terms of traffic evolution. Modeling the network only from a shallow spatial topological perspective ignores the important intrinsic association of the dynamic network. In this paper, we propose a semantic associative neural network (SANN) for traffic evolution analysis by modeling the propagation effects and similarity patterns between road units. Considering the inadequacy of the fixed adjacent matrix, graph convolution is used to encode the semantic features of a road network and embed them in a bidirectional recurrent neural network for sequence prediction. Finally, the experiments are conducted based on speed data sets to prove the effectiveness of the proposed method. The model achieved a well-predicted accuracy of 95.33% and 84.08% on Pems-Bay and Los Angeles data sets. [ABSTRACT FROM AUTHOR]

Dynamic charging has the potential to overcome the barriers to electric vehicle (EV) adoption by converting road segments into powered lanes (electric roadways or ERs). This paper focuses on examining public acceptance by determining the factors that affect the short-term and long-term intention to purchase an EV and drive on ERs; identifying the clusters of the technology acceptance; and assessing the value of travel time savings (VTTS) for driving on ERs, in general, and for different clusters. A survey was conducted in Los Angeles, California and distributed online, gathering 600 responses. The results showed that respondents' innovativeness, lifestyle and environmental consciousness significantly affect the intention to drive on ERs in the short- and long-run. Public acceptance, in general, seems to be related to charging patterns, safety of commute route, and safety concerns for ERs, among other factors, and depends on the implementation time of the technology. Higher VTTS were found for electrified lanes exclusive for EVs on interstate and arterial roads. People who were classified as positive toward ERs had higher VTTS for ERs followed by those who were neutral and those who were concerned about ERs. This paper can help policymakers and transport operators devise strategies to accelerate EV adoption by appropriately implementing the ER technology. [ABSTRACT FROM AUTHOR]

Traffic state estimation (TSE), which reconstructs the traffic variables (e.g., speed, flow) on road segments using partially observed data, plays an essential role in intelligent transportation systems. Generally, traffic estimation problems can be divided into two categories: model-driven approaches and data-driven approaches. The model-driven method is commonly used to solve TSE efficiently and calibrate the parameters of these models. The data-driven method requires a large amount of historical observed traffic data in order to improve performance accurately. In order to combine the advantages of model-driven and data-driven methods, this paper proposed a hybrid framework incorporating the traffic flow model into deep learning (TFMDL) modeling that contains both model-driven and data-driven components. This paper focuses on highway TSE with observed data from loop detectors. We build a hybrid cost function to adjust the weights of model-driven and data-driven proportions. We then evaluate the proposed framework using the open-access performance measurement system (PMS) dataset on a corridor of US I-405 in Los Angeles, California. The experimental results show the advantages of the proposed TFMDL approach in performing better than several benchmark models in terms of estimation accuracy and data efficiency. [ABSTRACT FROM AUTHOR]

Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way. Plain Language Summary: Earthquake nowcasting refers to the determination of hazard for major earthquakes at the present time, the recent past, and the near future. Nowcasting is an idea borrowed from economics, markets, and meteorology, where it has been frequently used. In this paper, we show that there is order hidden within chaotic earthquake seismicity using a very simple transformation of the data. Small earthquakes appear to transition from unstable stick‐slip events that produce seismic waves, to stable sliding where no seismic waves are produced. Our hypothesis is that this transition is due to a material phenomenon called strain‐hardening, that is frequently observed in laboratory rock mechanics experiments. The result is a state variable time series, computed over the last 51 years in California, that strongly resembles the long‐anticipated cycle of stress accumulation and release. Using supervised machine learning techniques, we can optimize the two‐parameter model. From that optimized model, we can rigorously calculate the probability of current hazard from major earthquakes. Extending these methods, we can also compute spatial hazard as well. The result is a new method for assessing earthquake hazard that may be useful for a variety of applications. Key Points: "Chaotic" seismicity contains hidden structure in the form of state variable time seriesStandard data science methods can be used to convert the time series to probabilitiesBoth temporal and spatial probabilities can be computed [ABSTRACT FROM AUTHOR]

From the onset of the COVID‐19 pandemic, policymakers and news reports warned that restrictions on individual mobility and commercial activity could lead to a surge in domestic violence (DV). Some initial studies found evidence of greater DV incidence during the pandemic, but findings were inconsistent across locations and DV measures. This paper focuses on a single major city, Los Angeles (LA), to study the impact of the initial pandemic shutdown. We use three DV measures from police data (911 calls, crime incidents and arrests) as well as two measures from non‐police administrative sources (the county DV hotline and hospital records). We find an increase in DV calls to the police and calls to the hotline. However, we find that DV crimes recorded by police and hospital visits by female assault (and DV) victims decreased significantly, suggesting that the increase in calls came from higher reporting rates. The decrease in DV crimes is not attributable to reductions in policing intensity, as the arrest rate for DV crimes was higher during the shutdown. We conclude that the initial LA shutdown increased the rates at which people contacted authorities about DV, but decreased the overall incidence of physical DV crimes in the population. [ABSTRACT FROM AUTHOR]

Atmospheric delay correction remains a major challenge for interferometric synthetic aperture radar (InSAR) technology. In this paper, we first reviewed several commonly used methods for tropospheric delay correction in InSAR. Subsequently, considering the large volume and high temporal resolution of global navigation satellite system (GNSS) station measurement data, we proposed a method for spatial prediction of the InSAR tropospheric delay phase based on the backpropagation (BP) neural network and GNSS zenith total delay (ZTD). Using 42 Sentinel-1 interferograms over the Los Angeles area in 2021 as an example, we validated the accuracy of the BP + GNSS method in spatially predicting ZTD and compared the correction effects of BP + GNSS and five other methods on interferograms using the standard deviation (StaD) and structural similarity (SSIM). The results demonstrated that the BP + GNSS method reduced the root-mean-square error (RMSE) in spatial prediction by approximately 95.50% compared to the conventional interpolation method. After correction using the BP + GNSS method, StaD decreased in 92.86% of interferograms, with an average decrease of 52.03%, indicating significantly better correction effects than other methods. The SSIM of the BP + GNSS method was lower in mountainous and high-altitude areas with obvious terrain changes in the east and north, exhibiting excellent and stable correction performance in different seasons, particularly outperforming the GACOS method in autumn and winter. The BP + GNSS method can be employed to generate InSAR tropospheric delay maps with high temporal and spatial resolution, effectively addressing the challenge of removing InSAR tropospheric delay signals in areas with significant terrain variations. [ABSTRACT FROM AUTHOR]

Parking supply is one of the most neglected elements of the built environment in travel behavior research, despite evidence linking parking with vehicle use. As transportation impacts of new development are increasingly measured by vehicle miles traveled (VMT), explicitly connecting parking characteristics with vehicle travel is necessary to better inform transportation and land use policy. In this paper, we begin to address this research gap and explore the relationship between constrained parking and household VMT. Utilizing the 2017 National Household Travel Survey (NHTS) California add-on sample, we estimate residential parking constraint for households in Los Angeles County. Then, we develop a two-level model framework. Level 1 (Cost) models estimate travel costs, represented by vehicle ownership as a function of parking constraints, the built environment, and demographics. Level 2 (Demand) models regress household-level total and homebased-work VMT on predicted vehicle ownership, controlling for temporal and environmental characteristics. To further explore the relationship between parking and VMT by place type, we applied Level 1 and Level 2 models to develop a suite of scenarios for typical households in Los Angeles County. Our findings support the hypothesis that the built environment (including parking) influences VMT through travel costs (vehicle ownership). Results from scenarios analysis reveal constrained on-site residential parking (< 1 parking space per dwelling unit), accounts for an approximate 10–23 percentage-point decrease in VMT within each place type. Finally, implications for practice and future research are presented. [ABSTRACT FROM AUTHOR]

This paper discusses the nonlinear dynamic seismic response of a 14‐story reinforced concrete core wall building designed with the prescriptive requirements of the 2019 California Building Code for a near‐fault site in Los Angeles, California, simulated with a detailed nonlinear analysis of the complete structure and subjected to an ensemble of three‐component ground motion input. The study follows up on a recently published work by the authors, which examined the response of the specific building to static lateral loads. The nonlinear model of the building was developed in the software FE‐Multiphys using a shell‐element implementation of the beam–truss model (BTM). The BTM has been extensively validated through prior studies for planar, flanged, and coupled walls under static and dynamic loading. The building model was subjected to an ensemble of ground motions scaled at the design and risk‐targeted maximum considered earthquake levels. The vertical ground motion component and the out‐of‐plane shear degradation of wall piers were included in the simulations. The analyses elucidate the profound effect of nonlinear flexure–shear interaction and of the multidirectional behavior of the flanged coupled core wall on the evolution and localization of damage and on residual deformations. The coupling behavior between the core wall, the slabs, and the columns is also analyzed, and the importance of modeling the nonlinear behavior of all slabs for the residual damage evaluation is demonstrated. The aim of this study is to improve the understanding of issues arising from prescriptive design requirements as well as of issues associated with limitations of standard nonlinear seismic analysis. [ABSTRACT FROM AUTHOR]

In February 2021, a winter storm brought snow, ice, and freezing temperatures, which caused severe interruptions in the electric power and water supply systems in Texas and surrounding areas. In this paper, we use survey data to investigate the ways that households adapted and reacted to those outages. The analysis aimed to determine (1) how common different household adaptations were, (2) how adaptations varied with outage and household characteristics, (3) how adaptations tended to occur together, (4) how unhappy households were as a result, (5) how household unhappiness varied with outage and household characteristics, and (6) what concerns influenced the unhappiness level. Results are compared with findings from a study that used an almost identical survey instrument but was based on a larger data set from Los Angeles. Findings from both studies suggest that almost everyone implemented at least one adaptation; most implemented several. They also agreed the most common adaptations were using candles, a flashlight, and/or a lantern; charging the cell phone in the car; purchasing bottled water; and delaying or reducing consumption. Both studies indicate that households experienced varied levels of unhappiness, which were similar for electric power and water interruptions. The reported levels of unhappiness were notably higher in Texas than Los Angeles, however, possibly because the outages had relatively long durations and were recent. Financial, time/effort, health, and stress concerns all were found to have a substantial influence on the extent of unhappiness, in both the Texas and Los Angeles analyses, suggesting that it is critical to consider all of them. Analysis of the Texas study introduced the new finding that repeated service outages during an event is associated with both increased adaptation implementation and greater unhappiness. Analyzing larger data sets from additional events in different locations would be helpful to further understanding of household experiences in service outages. Several practical applications emerge from this work. First, by knowing how people are likely to adapt, officials can better prepare to support their constituents during a crisis. Anticipating the distribution of unhappiness could point infrastructure operators to consider other measures for service quality besides downtime alone, as important as that is. Knowing the differential effects of outages on different populations can improve officials' understanding of social vulnerabilities in their communities, which is needed for planning, education, and outreach. Electric and water outages accompany many kinds of hazard events; the extent to which a local population can adapt, and how officials and infrastructure operators can enhance those adaptations, will be important elements of local community resilience. [ABSTRACT FROM AUTHOR]

Random forests are a popular type of machine learning model, which are relatively robust to overfitting, unlike some other machine learning models, and adequately capture non-linear relationships between an outcome of interest and multiple independent variables. There are relatively few adjustable hyperparameters in the standard random forest models, among them the minimum size of the terminal nodes on each tree. The usual stopping rule, as proposed by Breiman, stops tree expansion by limiting the size of the parent nodes, so that a node cannot be split if it has less than a specified number of observations. Recently an alternative stopping criterion has been proposed, stopping tree expansion so that all terminal nodes have at least a minimum number of observations. The present paper proposes three generalisations of this idea, limiting the growth in regression random forests, based on the variance, range, or inter-centile range. The new approaches are applied to diabetes data obtained from the National Health and Nutrition Examination Survey and four other datasets (Tasmanian Abalone data, Boston Housing crime rate data, Los Angeles ozone concentration data, MIT servo data). Empirical analysis presented herein demonstrate that the new stopping rules yield competitive mean square prediction error to standard random forest models. In general, use of the intercentile range statistic to control tree expansion yields much less variation in mean square prediction error, and mean square prediction error is also closer to the optimal. The Fortran code developed is provided in the Supplementary Material. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive framework for life‐cycle carbon emission assessment of steel frame structures in seismic zones, with a particular focus on emerging self‐centering steel structures with reduced residual deformation and enhanced seismic resilience. The proposed framework is illustrated through a life‐cycle embodied carbon (EC) emission study on an office building located at Los Angeles, USA. Different structural bracing systems are considered for comparison, namely, conventional concentrically braced frame (CBF), bucking‐restrained braced frame (BRBF), and self‐centering braced frames (SCBFs). The life cycle assessment (LCA) of EC emissions mainly involves four phases: (1) components manufacturing phase, (2) construction phase, (3) operation and maintenance phase, and (4) EC emissions related to seismic hazard. For the last stage, the engineering demand parameter (EDP) is obtained through incremental dynamic analysis (IDA), and combined with the fragility function and the seismic risk curve to obtain the expected EC emissions related to seismic hazard over the life cycle. Among other findings, the results show that: (1) In the manufacturing process, the EC emissions of the emerging SCBFs are slightly increased (by up to 1.4%) compared with the two other conventional steel frames. (2) During the construction, operation, and maintenance phases, there is no difference in the EC emissions for the different structural systems. (3) The EC emissions related to potential seismic risk are reduced by up to 65.3% when the proposed self‐centering structural system (P‐SCBF) is used. (4) Compared with the CBF, the total EC emission over a 100‐year lifespan can be reduced by up to 14.6% when the P‐SCBF is used. Due to the limited deformation capacity of braces, the EC emissions of CBF and BRBF are more sensitive to increases in the intensity measure (IM). Since a building becomes difficult to repair when the maximum residual inter‐story drift exceeds 0.5%, BRBF and CBF are more susceptible to demolition due to unacceptable residual deformation, leading to higher EC emissions. The EC reduction efficiency of the emerging steel frames become more remarkable with increasing life span. [ABSTRACT FROM AUTHOR]

This article chronicles the early journalism career of the first Chinese American newspaperwoman, Mamie Louise Leung, who reported for the Los Angeles Record from 1926 to 1929. Despite feeling insecure about her lack of journalism experience right out of college and intimidated by the idea of a Chinese girl not standing a chance among American news writers, Leung built a journalism career that spanned five decades. She proved to be a successful newspaperwoman with her intellect, skills, ambition, hard work, and the unique asset of being ethnically Chinese. Even though she was typecast by her bylined stories as a minority reporter covering topics related to the Chinese community in Los Angeles, she took charge of reporting for the important court beat for the Record throughout her tenure. [ABSTRACT FROM AUTHOR]

The use of polarimetric synthetic aperture radar (PolSAR) is one of the most successful tools for solving remote sensing problems. The multidimensional speckle noise encountered in the acquisition of these images is the main challenge for PolSAR users. Therefore, tailored processing of PolSAR images is required, especially for the use of hypothesis testing in change detection. In this paper, we use McKay's bivariate gamma distribution (MBG) to describe a joint distribution resulting from two components of the total scattering power image (SPAN). We derive closed form expressions for the MBG Kullback–Leibler and Rényi divergences between SPAN-based random pairs. We provide new two-sample divergence-based hypothesis tests and evaluate their performance using Monte Carlo experiments. Finally, we apply the new tests to real PolSAR images to evaluate the changes caused by urbanization processes in the Los Angeles and California regions. The results show that our proposals are able to detect changes in PolSAR images. [ABSTRACT FROM AUTHOR]

This paper investigates whether shocks in oil prices affect the level of crime in Kern County, California's top oil producing county. Monthly data from 2003 to 2018 is employed using a nonlinear autoregressive distributed lag model to test the asymmetric effects of these changes. Results for Kern County indicate that in the long-run, a negative shock in oil prices increases criminal activity while short-run oil price changes have no effect on criminal activity. Results from neighboring counties indicate that positive shocks to oil prices increase criminal activity in wealthy regions. Findings from generalized impulse response functions indicate that criminal activity in Los Angeles, Kings and San Bernardino Counties respond to shocks from Kern County's crime rates. They also indicate that criminal activity in Inyo and Los Angeles Counties influence Kern County's crime rate. [ABSTRACT FROM AUTHOR]

Recycled concrete aggregates (RCAs) have a high potential to be used as replacements for natural aggregates in the construction of pavements with similar or even better characteristics. This paper evaluates the influence of RCA as a partial replacement in a granular sub-base of 37.5 mm nominal maximum size (GSB38). Specifically, two replacement percentages of RCA by weight of the coarse aggregate of the GSB38 were evaluated: 10 and 15. To estimate the influence on the mechanical behavior, abrasion tests were carried out in the Los Angeles (LA) abrasion and California bearing ratio (CBR) methods. For GSB38, the average abrasion results were 30.54%. The CBR for the same material at a penetration of 0.254 cm was 26.90, 61.00 and 76.20% and at a penetration of 0.508 cm was 33.00, 65.70 and 82.00% for 10, 25 and 56 blows per layer, respectively. For GSB38 with 10% RCA, similar and even better behavior than natural GSB38 was found in terms of LA abrasion, with an average result of 38.68%. The CBR for the same alternative at a penetration of 0.254 cm was 13.20, 52.80 and 57.70% and at a penetration of 0.508 cm was 18.00, 61.30 and 68.90% for 10, 25 and 56 blows per layer, respectively. But GSB38 with 15% RCA showed an average abrasion of 45.51%, indicating a decrease compared with natural GSB38 and GSB38 with 10% RCA. The CBR for the same alternative also shows a decreasing trend. [ABSTRACT FROM AUTHOR]

Sedimentary phosphate extraction in open-pit operations generates large volumes of waste rock (WR), which are mainly overburdens and interburdens. Traditionally, the WR is mixed and stored on the surface in waste rock piles (WRPs). This paper presents a case study of the Benguerir mine site in Morocco. It investigates the potential valorization of each WR lithology based on the geological and geomechanical properties to reduce their environmental footprint and create added value to "waste." The WR samples (soils and rocks) were collected from drill cores and mining trenches in the Benguerir mine. The geological characterization results using petrographic descriptions indicate the presence of nine phosphate layers and, in addition to the overburdens, eight interburdens. Four types of WR are identified: carbonate, siliceous, marly clay, and phosphate. The geomechanical characterization of soil-like samples showed an average plasticity index (PI) of 50% according to the methylene blue value (MBV) of 7.1, classifying them in the A3–A4 categories as plastic and clayey marl soils. The hard rock samples have excellent mechanical properties in terms of their uniaxial compressive strength (UCS), Los Angeles abrasion value (LA), and micro-Deval value (MD). The average compressive strength is 104 MPa for the flint, 35 MPa for the phosphate flint, 32 MPa for the silexite, 26 MPa for the limestone, 11 MPa for the indurated phosphate, and 8 MPa for the marly limestone. Based on the obtained results, these WRs can be considered as an excellent alternative secondary raw material for use in civil engineering applications, ceramics, and cement industries. [ABSTRACT FROM AUTHOR]

Since the accessory dwelling unit (ADU) has emerged as a policy alternative to increase housing stock and provide affordable options for areas impacted by housing shortages, many local governments recently adopted ADU policies that promote the construction of ADUs. Taking the City of Los Angeles as the study area, this paper examines how the city's ADU ordinance impacts the relationship of the characteristics of the properties and neighborhoods with ADU development by constructing multilevel logistic regression models. The outputs of the models suggest that the ordinance contributes to diversifying the types and locations of the properties and neighborhoods where ADUs are built. The influence of the property characteristics associated with ADU development before the implementation of the ordinance significantly diminished after the ordinance. The outputs also indicate that the ordinance probably attracted ADU developments in the areas with higher accessibility to bus transit. These findings will help planners take appropriate actions and policies that support ADU developments. [ABSTRACT FROM AUTHOR]

The fifth-generation (5G) network is the current emerging technology that meets the increasing need for higher throughputs and greater system capacities. It is expected that 5G technology will enable many new applications and services. Vehicle-to-everything (V2X) communication is an example of an application that is supported by 5G technology and beyond. A V2X communication system allows a vehicle to be connected to an entity, such as a pedestrian, another vehicle, infrastructure, and a network, to provide a robust transportation solution. It uses many models and strategies that are usually based on machine learning (ML) techniques, which require the use of a vehicle dataset. In this paper, a real vehicle dataset is proposed that was generated in the city of Los Angeles (LA). It is called the Vehicle dataset in the city of LA (VehDS-LA). It has 74,170 samples that are located on 15 LA streets and each sample has 4 features. The LA dataset has been opened to allow researchers in V2X and ML fields to use it for academic purposes. The main uses of the VehDS-LA dataset are studies related to 5G networks, vehicle automation, or ML-Based vehicle mobility applications. The proposed dataset overcomes limitations experienced by previous related works. [ABSTRACT FROM AUTHOR]

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This paper presents the results on the demand survey obtained from owner–operator truck drivers for their willingness to use truck-only toll lanes on Southern California freeways. The stated preference survey method was used to estimate the values placed by them on time, reliability, and safety measures given nine different scenarios geared toward assessing those values. The data was collected by meeting in person with owner–operator truck drivers near the Ports of Los Angeles and Long Beach to understand their perspectives on truck-only toll lanes. The completed 31 survey responses out of 100 were used for analysis of variance and two sample t-tests. The results showed that 75.27% of the owner–operator truck drivers are willing to pay toll fees when they choose routes. The tolerated average toll fees are $13.77=h and $12.82=h for weekdays and weekends, respectively. The results showed that owner–operator truck drivers will prefer truck-only toll lanes in four out of six comparisons according to three measures despite sharing a common departure and destination. Drivers are willing to pay the highest toll fee of $0.31=mi or $18.35=h on any day when the main factor being compared is time measure. The toll fees associated with reliability and safety measures are $0.30=mi or $8.94=h and $0.22=mi or $11.01=h, respectively. These results are meaningful for legislators and transportation agencies as they will help better understand the contemporary issues and demands in estimating the utility of a truck-only toll lane. [ABSTRACT FROM AUTHOR]