<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?tc=NN%3AQbdd%2A" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. National Academy of Sciences. All rights reserved.</copyright><docs>http://blogs.law.harvard.edu/tech/rss</docs><managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor><webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster><image><title>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Responsibility Attribution for Autonomous Vehicle Crashes Based on Causal Inference: Case Study in California, USA</title><link>http://pubsindex.trb.org/view/2709310</link><description><![CDATA[In the autonomous driving environment, the attribution of responsibility becomes complex when multiple crash parties and factors are involved. This study proposes a method to attribute the responsibility of the primary crash vehicle when human drivers and autonomous driving systems coexist, and apply it to the existing Autonomous Vehicles (AVs) crash data from 2019 to 2023 in California, USA. Firstly, a causal network is constructed by integrating the Decision-making Trial and Evaluation Laboratory (DEMATEL) and Interpretative Structural Modeling (ISM) methods to analyze the mutual impact of factors in the crash data. Secondly, Random Forest (RF) is used to obtain the feature importance in AV crashes. Based on the relationship between factors and the main responsible parties, the responsibility among relative stakeholders can be quantified. Under the research data in California, in autonomous driving mode, human drivers of the primary crash vehicle and software developers both account for 31% of the crash. Following behind are other stakeholders at 21% and vehicle manufacturers at 17%. On this basis, adjustments can be made to the responsible proportion in relation to a specific crash. By identifying the impact factors of AV crashes and responsibility attribution, this study offers important insights into safe autonomous driving tests, AV production regulation, and the development of crash responsibility policies. The methodology framework developed in this paper is universal and can be applicable to AV crash analysis in diverse regions and AV penetration rates.]]></description><pubDate>Wed, 03 Jun 2026 09:07:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709310</guid></item><item><title>Optimization of Emergency Bus Bridging Service with Mixed Diesel–Electric Fleets under Metro Disruptions</title><link>http://pubsindex.trb.org/view/2709234</link><description><![CDATA[Metro service disruptions occur frequently in large cities, making emergency bus bridging services essential for maintaining network functionality and reducing passenger delays. With the rapid electrification of urban bus fleets, traditional bus bridging strategies face new challenges related to electric bus state-of-charge (SOC) limits and charging availability, which are often ignored in existing models. This study develops a mixed-fleet optimization framework for emergency bus bridging services that jointly considers diesel buses and electric buses (EBs). Two models are formulated: a standard feeder model (SFM) providing all-stop services, and a combined feeder model (CFM) that supplements standard feeders with limited-stop direct services to serve high-demand origin–destination (OD) pairs. Both models explicitly incorporate EB SOC constraints, charging requirements, and coordinated fleet deployment. A case study based on the December 2023 rear-end collision on the Beijing Subway Changping Line demonstrates that both models can generate feasible emergency response plans under realistic operational and charging constraints. Compared with the SFM, the CFM reduces total system cost from RMB 76,802.99 to 76,752.93. This improvement is driven by a reduction of RMB 322.98 in passenger delay cost, which outweighs an increase of RMB 272.93 in operator cost. Sensitivity analyses are further conducted to evaluate model performance under varying demand patterns, disruption durations, direct-service capacities, and charger availabilities. Results indicate that the CFM performs best under concentrated demand, long-distance trips, and moderate disruption durations, highlighting its effectiveness in reducing passenger delays for high-impact OD flows.]]></description><pubDate>Mon, 01 Jun 2026 16:52:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2709234</guid></item><item><title>Effect of Emergency Vehicle Lighting, Marking, and Message/Arrow Boards in Reducing Responder-Involved Crashes: Systematic Literature Review and National Level Survey</title><link>http://pubsindex.trb.org/view/2706085</link><description><![CDATA[Emergency responders play a vital role in roadway safety and incident management but remain highly vulnerable to secondary crashes and struck-by incidents. Despite laws such as Florida’s “Move Over” law, fatality and injury rates among responders remain high. This study presents a systematic literature review and a national expert survey examining emergency vehicle lighting, arrow boards, and retroreflective markings on responder safety. From an initial pool of 224 publications, 52 were synthesized. Key findings indicate that lighting configurations—specifically color, flash pattern, flash rate, and placement—significantly influenced driver behavior and compliance with the move-over law. Additionally, the use of arrow boards and retroreflective tapes, along with patterns and chevron markings, significantly improved vehicle visibility and conspicuity of the responder vehicles. The national survey reinforced these findings by revealing the broad variability in lighting systems, arrow boards, and retroreflective markings used, along with variations in cone deployment and other safety measures, across safety service patrol programs. Agencies emphasized the need for national standards and the integration of automated technologies to improve safety and efficiency. Future research should evaluate the influence of field variables such as geometric design, weather conditions, driver demographics, and human factors to guide the development of future policies and standards for emergency-responder safety.]]></description><pubDate>Wed, 27 May 2026 13:06:57 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706085</guid></item><item><title>Traffic Object Detection Across Diverse Environmental Conditions: Comparative Performance Evaluation of Computer-Vision Models</title><link>http://pubsindex.trb.org/view/2706173</link><description><![CDATA[This paper introduces a novel evaluation framework for systematically assessing the performance of computer-vision models in traffic-surveillance and autonomous-vehicle applications under varying environmental conditions. The framework integrates synthetic-data generation with structured experiments to analyze model performance across different weather and lighting scenarios, addressing the lack of controlled datasets for true apple-to-apple comparisons. The results obtained based on synthetic and real-world video data reveal key trade-offs between detection accuracy, inference time, and environmental resilience across different model categories. One-stage models, such as YOLO, exhibit high inference speed but suffer significant performance degradation in adverse weather, whereas RetinaNet and SSD demonstrate greater robustness. Two-stage models, particularly Faster R-CNN, provide higher accuracy and stability but require longer inference times. Diffusion-based models fail in surveillance-camera views, highlighting training limitations. Also, illumination changes affect detection performance less than rain and snow, likely because of better representation of lighting variations in training datasets. This work is among the first to systematically quantify performance degradation under controlled environmental conditions, providing valuable insights for real-world deployments in intelligent transportation systems.]]></description><pubDate>Wed, 27 May 2026 10:48:02 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706173</guid></item><item><title>Substitution or Shared Utilization? Intrahousehold Vehicle Use in Mixed-Powertrain Households</title><link>http://pubsindex.trb.org/view/2705420</link><description><![CDATA[While previous research has focused heavily on understanding the factors deriving alternative fuel vehicle adoption rates, there remains a significant gap in understanding how households distribute mileage across different powertrains. This study utilizes data from the 2022 Next Generation National Household Travel Survey to investigate vehicle miles traveled within a sample of 150 plug-in electric vehicle (PEV)-owning households (in which at least one battery electric vehicle is present), characterizing how different powertrains are integrated into daily mobility. Leveraging a Seemingly Unrelated Regression (SUR) framework the study jointly models the utilization of PEVs, hybrid electric vehicles (HEV), and internal combustion engine vehicles (ICEVs) while accounting for household-level substitution effects. The results provide evidence of an asymmetric substitution effect. In households with mixed-powertrain configurations, the ICEV captures a substantially higher share of household miles (compared with the PEV), acting as a utility sponge. Conversely, the model identifies specific socioeconomic and geographic cohorts that prioritize PEV as the primary household workhorse, indicating a systematic sorting effect. Although the sample size limits broader generalizability, these findings suggest that PEVs are used for frequent, specific routine-intensive roles, whereas the ICEV remains a specialized utility vehicle. These insights highlight distinct intrahousehold vehicle use behaviors that are often obscured by aggregate fleetwide statistics.]]></description><pubDate>Tue, 26 May 2026 09:44:22 GMT</pubDate><guid>http://pubsindex.trb.org/view/2705420</guid></item><item><title>Developing a Guide for Local Truck Parking Regulations</title><link>http://pubsindex.trb.org/view/2703936</link><description><![CDATA[This report presents a report on local truck parking regulations that includes model ordinances, zoning code provisions, and strategies to support decisionmaking and implementation at the local level. The report was developed through a research approach that included a regulatory scan, case studies of local jurisdictions, and the creation of model ordinances and policy tools. This report and accompanying guide are intended to assist agencies and communities in understanding truck parking logistics, engaging stakeholders, and incorporating truck parking considerations into local plans and development codes. These deliverables will be of particular interest to state departments of transportation (DOTs), local government planning officials, metropolitan planning organizations (MPOs), zoning administrators, industrial developers, and freight and logistics professionals.]]></description><pubDate>Sat, 23 May 2026 18:35:19 GMT</pubDate><guid>http://pubsindex.trb.org/view/2703936</guid></item><item><title>Guide for Local Truck Parking Regulations</title><link>http://pubsindex.trb.org/view/2703937</link><description><![CDATA[Truck parking is a critical transportation issue with economic and social impacts at the local and national levels—in 2023, the U.S. Bureau of Labor Statistics reports that one in 16 workers was employed in the trucking industry. Concerns and considerations around truck parking are influenced by growth in population and economic activity, and amplified by recent events and trends like the Coronavirus (COVID-19) pandemic and the rise of e-commerce and global trade. A greater need for truck activity highlights the very human need for safe and secure parking for truck drivers. Although there are now more trucks on roads than ever before, there has not been a corresponding expansion in available truck parking. Studies report 11 truck drivers for every one parking space, and most drivers reported issues with finding safe truck parking. Building truck parking is often at conflict with land use goals in densely populated areas and constrained by local ordinances that may not reflect current parking demand. Effective local truck and trailer parking ordinances help keep truckers and other drivers safe, improve highway performance, reduce road maintenance costs, support economic growth, and promote community health and livability. This Guide presents a solutions toolbox and a range of model truck parking and staging ordinances, rules, and regulations suitable for consideration and adoption by local municipalities. The information documented in this Guide stems from a scan of available literature and local government policies, as well as stakeholder engagement with local agencies, truck drivers, and freight facility operators. This Guide is centered around truck parking needs for short-haul, long-haul, and drayage truck drivers in relation to the following key topic areas: staging, queuing, emergency road closure, vehicle breakdown, rest, human needs, and off-duty parking. Appendices present detailed information regarding case studies, infographics describing various truck types, and model ordinances, regulations and policies.]]></description><pubDate>Sat, 23 May 2026 18:35:19 GMT</pubDate><guid>http://pubsindex.trb.org/view/2703937</guid></item><item><title>The Acceptance of Level 3 Autonomous Vehicles: Integrated Framework of Task-Technology Fit and User-Technology Fit Perspectives</title><link>http://pubsindex.trb.org/view/2706049</link><description><![CDATA[Autonomous vehicles (AVs) face major adoption barriers in emerging markets as a result of weak infrastructure, limited user exposure, and regulatory uncertainty. To explain adoption in such contexts, this study extends the Task-Technology Fit (TTF) model by integrating a User-Technology Fit (UTF) construct that captures the psychological and capability alignment between users and AV technology, while examining trust in automation as a moderating factor. A cross-sectional online survey was conducted with 227 licensed drivers in Ethiopia (18 to 56+ years; 74% male; average driving experience = 7.5 years). The questionnaire incorporated validated scales corresponding to the proposed research model and was distributed over three months. Results indicate that higher UTF significantly enhances perceptions of TTF, and both fit constructs positively influence the intention to adopt Level 3 AVs. Mediation analysis confirms that TTF partially mediates the effect of UTF on adoption intention. Trust strengthens the positive influence of TTF on adoption but does not significantly moderate the UTF-intention link. Integrating UTF into the TTF framework advances adoption theory by offering a holistic “technology fit” (FIT) perspective that unites psychological and functional alignment. The findings also highlight trust’s conditional influence and underscore the importance of designing technologies that align with both user and task dimensions to foster adoption in low-exposure markets.]]></description><pubDate>Sat, 23 May 2026 18:35:18 GMT</pubDate><guid>http://pubsindex.trb.org/view/2706049</guid></item><item><title>Navigating Ethics in Autonomous Vehicles: Data-Driven Prospect Theory Approach to Decision Making</title><link>http://pubsindex.trb.org/view/2704072</link><description><![CDATA[As autonomous vehicles (AVs) increasingly interact with pedestrians and other vulnerable road users, ensuring that their decisions align with human ethical expectations has become a critical challenge. Traditional AV planning approaches prioritize safety but often neglect the behavioral and moral complexity of real-world traffic interactions. This study introduces a novel ethical evaluation framework for AV behavior that integrates prospect theory (PT), a behavioral model of decision making under risk, with two foundational ethical paradigms: utilitarianism and deontology. Using high-resolution trajectory data from the Top-View Trajectories dataset, we analyzed 714 pedestrian–vehicle interactions at an intersection. For each interaction, PT-based utility functions were computed for both the study agent and the overall system, accounting for travel time and time-to-collision as proxies for efficiency and safety. These utilities were combined into a composite ethical score, enabling the classification of behavior along a continuous ethical spectrum. Our results revealed that safety, as measured by time-to-collision, strongly influenced ethical evaluations, but that prolonged inefficiency was also penalized. Vehicle-yielded interactions consistently received higher ethical scores, supporting the potential of social AV behavior. The framework additionally identified cases of ethical divergence (where agents benefit at the system’s expense), highlighting the need for decision models that balance self-interest and collective outcomes. This work offers a scalable, data-driven approach to evaluating AV ethics and lays the groundwork for ethically aligned AV planning.]]></description><pubDate>Thu, 21 May 2026 09:09:19 GMT</pubDate><guid>http://pubsindex.trb.org/view/2704072</guid></item><item><title>Sex and Age Differences in Attention Allocation during Highway Lane-Change Process: An Eye-Tracking Study</title><link>http://pubsindex.trb.org/view/2704068</link><description><![CDATA[Lane-change maneuvers represent high-risk operations in highway driving, with drivers’ attention allocation strategies playing a crucial role in safety. In this study, driving simulation and eye-tracking technology were employed to investigate visual attention allocation patterns during lane-change execution in 38 drivers (26 men, 12 women; 21 young adults aged 20–36 years, 17 middle-aged adults aged 37–62 years). The lane-change process was divided into pre-change and post-change phases, with seven areas of interest established. Statistical analyses employed nonparametric tests based on normality assessments to examine sex and age effects. The findings reveal distinct attention allocation patterns. (1) Male drivers exhibited a mirror-dependent monitoring pattern, with fixation time ratios on rearview mirrors reaching 20.5% during right lane-change preparation, approximately 2.4 times higher than that of women (8.4%), enabling gap assessment. Female drivers demonstrated a forward-concentrated pattern with parameter verification, allocating 62.3% attention to the front target lane while maintaining systematic dashboard monitoring (up to 4.4%) for speed verification. (2) Young drivers displayed forward-concentrated patterns with parameter verification, maintaining over 70% attention to the front target lane. Middle-aged drivers exhibited multireference integrated patterns, with significantly higher reliance on target lane lines and mirrors (14.8% versus 10.6% for youth), reflecting mature strategies developed through driving experience. Cross-study comparisons with actual road research validated these patterns’ robustness. These findings provide empirical foundations for personalized advanced driver assistance systems design and targeted driver training programs, offering theoretical and practical implications for enhancing highway driving safety.]]></description><pubDate>Thu, 21 May 2026 09:09:18 GMT</pubDate><guid>http://pubsindex.trb.org/view/2704068</guid></item><item><title>Traffic Flow Impacts of Autonomous Utility Service Vehicles Representing Low-Speed Operation in Urban Networks</title><link>http://pubsindex.trb.org/view/2703798</link><description><![CDATA[Autonomous utility service vehicles (AUSVs) represent a distinct class of automated platforms designed to operate at very low speeds (often around 5–10 km/h) for urban road maintenance tasks, yet their integration in mixed traffic remains poorly understood. This study evaluates the effect of AUSVs through a microscopic simulation using a real-world network modeled on the Pangyo autonomous driving testbed in South Korea. Twelve scenarios (six AUSV speeds, 5–30 km/h, and two traffic volumes, level of service [LOS] C and LOS D) were simulated; LOS C and D were deliberately selected—beyond the typical off-peak LOS A/B context—to empirically identify the demand threshold above which AUSV effects become operationally significant. Performance was assessed across vehicle, lane, link, and network levels using speed, shockwave propagation, time-to-collision (TTC) ratios, and lane-change rates. Results revealed consistent patterns. At the lane level, AUSVs at 5 km/h induced backward-moving queues up to 120 m in length and critical TTC ratios of up to 0.50 under LOS D, while no significant disturbances occurred under LOS C—revealing a critical operational threshold between the two demand regimes. At the link level, speed reductions of ≥20 km/h concentrated on two-lane bottlenecks, where overtaking opportunities were constrained. Network-wide, average speeds increased monotonically with AUSV speed, while lane-change rates peaked at 5 km/h (32.5 events/km under LOS C; 59.1 events/km under LOS D) and declined thereafter. These findings support demand-aware scheduling (off-peak for ≤10 km/h operation), minimum speed thresholds (≈15 km/h), and bottleneck-avoiding routes as practical strategies for sustainable AUSV deployment.]]></description><pubDate>Mon, 18 May 2026 14:04:39 GMT</pubDate><guid>http://pubsindex.trb.org/view/2703798</guid></item><item><title>Shared Autonomous Vehicle Scheduling with Heterogeneous Ride-Sharing Preferences and Mixed-Capacity Fleet Sizing</title><link>http://pubsindex.trb.org/view/2701377</link><description><![CDATA[With the rapid advancement of shared autonomous vehicle (SAV) technology and its great potential in optimizing urban transportation, scheduling models that rely solely on single-capacity vehicles struggle to effectively accommodate passengers’ heterogeneous ride-sharing preferences. This paper proposes a mixed-capacity SAV scheduling strategy that accounts for heterogeneous ride-sharing preferences, aimed at balancing diverse passengers’ ride-sharing willingness and maximizing the transportation benefits of shared mobility. By introducing the concept of “weighted edge contraction,” the weighted graph structure for solving the ride-sharing matching problem is simplified. First, by considering passengers’ heterogeneous ride-sharing preferences that encompass no ride-sharing, 2-passenger ride-sharing, 4-passenger ride-sharing scenarios, we analyzed system performance across different fleet configurations. The results demonstrated that deploying a mixed fleet with capacities of two and four passengers led to better performance across both system efficiency and service quality metrics. This strategy not only reduced the total number of vehicles in the network, decreased overall travel distance, and lowered fuel consumption, it also lowered passenger travel fares without compromising average waiting times. Next, by considering differences in passengers’ willingness to ride-share, we analyzed how system performance changed with different ride-sharing request ratios. The results revealed that higher ride-sharing acceptance rates facilitated travel request consolidation, leading to fewer deployed vehicles, shorter travel times and distances, and reduced energy consumption. Therefore, our proposed mixed-capacity vehicle scheduling strategy offers theoretical insights for optimizing SAV operations across diverse request scenarios.]]></description><pubDate>Fri, 15 May 2026 09:18:58 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701377</guid></item><item><title>Route-Constrained Optimization Models for Electric School Bus Allocation and Stop Sequencing</title><link>http://pubsindex.trb.org/view/2701299</link><description><![CDATA[This study underscores the growing need to transition from conventional school buses powered by internal combustion engines to electric school buses (ESBs). We propose mixed integer programming (MIP)-based approaches to optimize the allocation of ESBs to predetermined routes while determining their travel paths and bus stop sequences. The problem formulation incorporates heterogeneous ESBs with mixed student loading. Essential factors such as bus seating capacity, battery capacity, energy consumption cost, and procurement cost are considered to determine optimal bus selection and allocation strategies that minimize total expenses. To address this challenge, we introduced two methodologies: a MIP-based optimization model and a two-stage hybrid model that integrates optimization with heuristic approaches. We evaluate the efficiency of the proposed approaches through nineteen computational scenarios and compare their performance. The results indicate that the hybrid model significantly improves computational efficiency for large-scale problem instances, demonstrating its potential as a scalable tool for strategic planning.]]></description><pubDate>Fri, 15 May 2026 09:18:58 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701299</guid></item><item><title>Investigating Flexible Pavement Responses and Performance under Trunnion Axle Loading Using Three-Dimensional Finite Element Modeling and Field Validation</title><link>http://pubsindex.trb.org/view/2701381</link><description><![CDATA[The growing demand for high-capacity freight vehicles has heightened the need to evaluate the impact of heavy truck axle configurations on pavement behavior. This study employed a validated three-dimensional (3D) finite element (FE) model to evaluate the structural response of flexible pavements under trunnion and tandem axle configurations at two vehicle speeds (35 and 55 mph), using legal load levels of 60 and 34 kip, respectively. Then, key pavement responses (i.e., tensile strain, stress, and vertical displacement) were assessed. Also, pavement performance (i.e., fatigue cracking and subgrade rutting) was evaluated. The results showed that the trunnion axle generated higher tensile strain, von Mises stress, maximum principal stress, vertical stress, and vertical displacement than the tandem axle. This increase in pavement responses is primarily attributed to the trunnion’s shorter axle spacing, which causes overlapping stress zones and amplifies strain concentrations within the asphalt and subgrade layers. Concerning pavement performance, the trunnion axle exhibited lower fatigue life and lower resistance to subgrade rutting. Vehicle speed was also found to influence pavement response, with lower speeds producing higher stress, strain, and displacement levels for both axle types. Model predictions were validated using field measurements from the MnRoad test site in Minnesota, USA, confirming consistent trends in pavement responses under dynamic axle loading. These findings offer insight into the roles of axle configuration, maximum load, and truck speed in determining flexible pavement performance, thereby supporting better-informed design and evaluation practices.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701381</guid></item><item><title>Express Charging Lanes for Electric Vehicle Evacuation</title><link>http://pubsindex.trb.org/view/2701379</link><description><![CDATA[The long-distance evacuation of electric vehicles (EVs) presents significant challenges for disaster management owing to their limited driving range and constrained charging infrastructure. EVs with long charging times can create negative externalities for all other EVs waiting in queues, especially during evacuation scenarios. This study investigates the use of express charging lanes to reduce overall evacuation delays. We propose optimization models to optimize the allocation of charging plugs for express and regular charging, considering both user-equilibrium and system-optimal scenarios. To account for heterogeneities and uncertainties, such as stochastic EV arrival patterns and variable charging demands, we further develop numerical simulation models to quantify the delay distribution. We found that separating EVs with lower charging demand had the potential to minimize total system delay. The proposed models identified the optimal level of express charging plug allocation to minimize the total charging delay without centralized enforcement of traffic distribution. In addition, the models could enable government agencies to estimate the required charging resources to fulfill an evacuation within a given time window. The insights generated by the proposed theoretical models were validated using agent-based simulation, in which uncertainties could be flexibly represented.]]></description><pubDate>Thu, 14 May 2026 17:01:53 GMT</pubDate><guid>http://pubsindex.trb.org/view/2701379</guid></item></channel></rss>