International Journal of Transportation Science and Technology最新文献

A large-scale analytical residential parcel delivery model evaluating greenhouse gas emissions, COVID-19 impact, and cargo bikes 评估温室气体排放、新冠肺炎影响和货运自行车的大型分析住宅包裹交付模型

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.08.002

The e-commerce industry has experienced significant growth in the past decade, particularly post-COVID. To accommodate such growth, the parcel delivery sector has also grown rapidly. However, there is a lack of study that properly evaluates its social and environmental impacts at a large scale. A model is proposed to analyze such impacts. A parcel generation process is presented to convert public data into parcel volumes and stops. A continuous approximation model is fitted to estimate the length of parcel service tours. A case study is conducted using New York City (NYC) data. The parcel generation is shown to be a valid fit. The continuous approximation model parameters have R² values of 98% or higher. The model output is validated against UPS truck trips. Application of the model to 2021 suggests residential parcel deliveries contributed to 0.05% of total daily vehicle-kilometer-traveled (VKT) in NYC corresponding to 14.4 metric tons of carbon equivalent (MTCE) emissions per day. COVID-19 contributed to an increase in parcel deliveries that led to up to 1 064.3 MTCE of annual greenhouse gas (GHG) emissions in NYC (which could power 532 standard US households for a year). The existing bike lane infrastructure can support the substitution of 17% of parcel deliveries by cargo bikes, which would reduce VKT by 11%. Adding 3 km of bike lanes to connect Amazon facilities can expand their cargo bike substitution benefit from a VKT reduction of 5% up to 30%. If 28 km of additional bike lanes are made, parcel delivery substitution citywide could increase from 17% to 34% via cargo bike and save an additional 2.3 MTCE per day. Cargo bike priorities can be set to reduce GHG emissions for lower-income neighborhoods including Harlem, Sunset Park, and Bushwick.

在过去十年中，电子商务行业经历了显著增长，尤其是在经历了《电子商务发展协议》（COVID）之后。为了适应这种增长，包裹递送行业也迅速发展。然而，目前还缺乏对其大规模的社会和环境影响进行适当评估的研究。本文提出了一个模型来分析这种影响。本文提出了一个包裹生成过程，将公共数据转换为包裹数量和停靠站点。采用连续近似模型来估算包裹服务巡视的长度。利用纽约市（NYC）的数据进行了案例研究。结果表明，包裹生成的拟合是有效的。连续近似模型参数的 R2 值达到 98% 或更高。模型输出与 UPS 卡车行程进行了验证。该模型在 2021 年的应用表明，住宅包裹递送量占纽约市日车辆行驶总公里数（VKT）的 0.05%，相当于每天 14.4 公吨碳当量（MTCE）的排放量。COVID-19 促使包裹递送量增加，导致纽约市每年温室气体排放量高达 1 064.3 公吨碳当量（GHG）（可供 532 个标准美国家庭使用一年）。现有的自行车道基础设施可以支持用货运自行车替代 17% 的包裹运送，这将减少 11% 的总运输量。增加 3 公里的自行车道连接亚马逊设施，可将货运自行车的替代效益从减少 5% 的 VKT 提高到 30%。如果增加 28 公里的自行车道，全市范围内通过货运自行车运送包裹的替代率可从 17% 提高到 34%，每天可额外节省 2.3 MTCE。货运自行车的优先级可设定为减少哈莱姆区、日落公园和布什威克等低收入社区的温室气体排放。

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引用次数: 0

Risk analysis of bridge maintenance accidents: A two-stage LEC method and Bayesian network approach 桥梁维修事故风险分析:两阶段LEC法和贝叶斯网络方法

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.07.002

Bridge maintenance is a long-term process that is prone to accidents. Identifying and reducing hidden dangers is crucial in decreasing the occurrence of such accidents. This study proposes a two-stage risk evaluation model based on the likelihood exposure consequence (LEC) method, which includes an occurrence stage and a development stage. The model utilizes hidden danger data accumulated over a long period to reflect the current maintenance stage's risk level. Additionally, a risk prediction model based on the Bayesian network is established to better identify hidden dangers that have a significant impact on construction risk levels (CRLs). The models are validated using 50 weeks of hidden danger data obtained from a real-world bridge maintenance project. The results show that certain hidden dangers have high risk levels when the CRL is high, and small changes in the risk level of certain hidden dangers can have a significant impact on the CRL. This study's models can aid in the development of more targeted HD prevention measures.

桥梁维护是一个长期过程，容易发生事故。识别和减少隐患对于减少此类事故的发生至关重要。本研究提出了一种基于可能性暴露后果（LEC）方法的两阶段风险评估模型，包括发生阶段和发展阶段。该模型利用长期积累的隐患数据来反映当前维护阶段的风险水平。此外，还建立了一个基于贝叶斯网络的风险预测模型，以更好地识别对施工风险等级（CRL）有重大影响的隐患。利用从实际桥梁维护项目中获取的 50 周隐患数据对模型进行了验证。结果表明，当 CRL 较高时，某些隐患的风险水平较高，而某些隐患风险水平的微小变化会对 CRL 产生重大影响。这项研究的模型有助于制定更有针对性的高清预防措施。

引用次数: 0

Mining motif periodic frequent travel patterns of individual metro passengers considering uncertain disturbances 考虑不确定干扰的地铁个体乘客母题周期频繁出行模式挖掘

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.07.005

Periodic pattern mining is of great significance for understanding passenger travel behavior, but the previous works mainly focused on the trajectory data and the dimension of the spot/point. Besides, many uncertain factors (severe weather, traffic accident, etc.) may interfere with discovering original and accurate periodic travel patterns. This paper proposes a novel type of travel pattern called motif periodic frequent pattern (MPFP), which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics, considering, uncertain disturbances. We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data (SCD), and apply the real long-time-span experimental data from a large-scale metro system is applied. Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of “week”. Only the top 10 types of all 4 624 types account for about 95% of all motifs and the top 5 types constitute about 90%, and the MPFP of the top 3 types of motifs account for nearly 80% of all periodic patterns, in which Mono-MPFP and 2-MPFP are the main ones. The relatively stable time range of MPFP is three months, and the threshold for the optimal uncertain disturbance factor should be set at 5%. Additionally, several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.

周期性模式挖掘对于理解乘客的出行行为具有重要意义，但以往的工作主要集中在轨迹数据和点/点的维度上。此外，许多不确定因素（恶劣天气、交通事故等）可能会干扰发现原始、准确的周期性出行模式。本文提出了一种新型的出行模式--周期性频繁模式（Motif periodic frequent pattern，MPFP），它能捕捉到具有高阶时空特征的地铁乘客个体的网络时空模式的周期性，并考虑了不确定的干扰因素。我们还提出了一种新的完整挖掘算法 MPFP-growth，用于从智能卡数据（SCD）中提取 MPFP，并应用了大规模地铁系统的真实长时跨实验数据。结果表明，经常乘坐地铁的乘客通常会有一些典型的 MPFP，这些 MPFP 具有 "周 "的时间周期特征。在全部 4 624 种类型中，只有前 10 种类型的主题词约占全部主题词的 95%，前 5 种类型的主题词约占 90%，前 3 种类型的主题词 MPFP 占全部周期性模式的近 80%，其中以 Mono-MPFP 和 2-MPFP 为主。MPFP 相对稳定的时间范围为 3 个月，最佳不确定干扰因子的阈值应设定为 5%。此外，还介绍了几种有趣的地铁乘客个人典型 MPFP 及其比例，以进一步了解 MPFP 的多种变体。

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引用次数: 0

Evaluation of false alarm alarms in truck FCW based on calibration of RSS model under different driving scenarios 基于不同驾驶场景下 RSS 模型校准的卡车 FCW 误报警报评估

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.07.001

Advanced driver-assistance systems (ADASs), such as forward collision warning (FCW), are widely used and, in some countries, have been made mandatory for commercial vehicles. In practical applications, however, FCW systems produce many false alarms. Using scenario and driving behavior data collected from naturalistic driving study data of trucks, a variable threshold evaluation method was proposed to determine the factors correlating with false alarms. A total of 450 collision avoidance events were divided based on driving characteristics into three groups with k-means clustering. Responsibility-sensitive safety (RSS) model’s parameters were calibrated with the driving behavior characteristics and scenarios to evaluate the truck FCW system’s alarm accuracy. The evaluation of the results of truck FCW system based on RSS model found 47 false alarm alarms in the 450 events, a false alarm rate of 11.19%. When the following distance was close (<7 m) or far (>20 m), the false alarm rate reached more than 30%. The minimum time to collision (TTC) in the close distance driving clusters (DCs) (5.81 s) was lower than that in long distance DCs (7.68 s and 9.46 s). Braking force in the low-speed DCs (deceleration at −0.16 g and −0.55 g) was lower than in high-speeded DC (deceleration = −1.21 g). The FCW system does not conform to the driver's reaction time and braking characteristics in different scenarios, and is the main reason for false alarms. This is more obviously reflected in low-speed short distance and high-speed long-distance scenarios.

先进驾驶辅助系统（ADAS），如前撞预警（FCW），已得到广泛应用，在一些国家，商用车辆已强制使用该系统。然而，在实际应用中，FCW 系统会产生许多误报。利用从卡车自然驾驶研究数据中收集的场景和驾驶行为数据，提出了一种可变阈值评估方法，以确定与误报相关的因素。通过 k-means 聚类，根据驾驶特征将总共 450 个避免碰撞事件分为三组。根据驾驶行为特征和场景对责任敏感安全（RSS）模型的参数进行校准，以评估卡车 FCW 系统的报警准确性。基于 RSS 模型的卡车 FCW 系统结果评估发现，在 450 个事件中有 47 个误报，误报率为 11.19%。当跟车距离较近（7 米）或较远（20 米）时，误报率达到 30% 以上。近距离驾驶集群（DCs）的最小碰撞时间（TTC）（5.81 秒）低于远距离驾驶集群（DCs）的最小碰撞时间（7.68 秒和 9.46 秒）。低速行驶集群的制动力（减速度为-0.16 g和-0.55 g）低于高速行驶集群（减速度=-1.21 g）。FCW 系统不符合驾驶员在不同情况下的反应时间和制动特性，是造成误报的主要原因。这一点在低速短距离和高速长距离场景中体现得更为明显。

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引用次数: 0

Are current microscopic traffic models capable of generating jerk profile consistent with real world observations? 目前的微观交通模型是否能够生成与实际观测结果一致的抽动曲线？

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.08.008

Microscopic behavior modeling plays a critical role in traffic flow analyais, simulation, and autonomous vehicle algorithm development. Numerous efforts are devoted to the development of it in both longitudinal and lateral dimensions. Empirical observations reveal that jerk (the differential of acceleration) significantly influences traffic safety, with a speed-dependent jerk profile observed in both longitudinal and lateral movements. Replication of the speed-dependent jerk profile is crucial when the microscopic models are employed to the analysis of traffic safety. However, this research shows that current stochastic microscopic models cannot describe speed-dependent jerks, and thus cannot be directly used to describe driving behavior with considerable jerk profiles. This research firstly derives the jerk distribution for a general stochastic car following (CF) model, and then shows that several CF models together with lateral movement model cannot generate the realistic jerk distribution. A compound Poisson formulation is proposed to remedy the drawbacks of these models. The model consists of a diffusion part and a jump part. The former describes normal driving stochasticity, while the latter describes driving involving high jerk. The numerical studies show that the proposed model can replicate the speed-dependent jerk phenomenon. The propagation of the behavior in the traffic flow is also investigated.

微观行为建模在交通流分析、模拟和自动驾驶汽车算法开发中发挥着至关重要的作用。人们在纵向和横向维度上都投入了大量精力进行开发。经验观察表明，颠簸（加速度差）对交通安全有很大影响，纵向和横向运动中都能观察到与速度相关的颠簸曲线。在使用微观模型分析交通安全时，复制与速度相关的颠簸曲线至关重要。然而，本研究表明，目前的随机微观模型无法描述与速度相关的颠簸，因此不能直接用于描述具有相当颠簸轮廓的驾驶行为。本研究首先推导了一般随机汽车跟随（CF）模型的颠簸分布，然后证明了几种 CF 模型和横向运动模型无法生成真实的颠簸分布。研究提出了一种复合泊松公式来弥补这些模型的缺陷。该模型由扩散部分和跳跃部分组成。前者描述正常行驶的随机性，后者描述涉及高颠簸的行驶。数值研究表明，所提出的模型可以复制与速度相关的颠簸现象。此外，还研究了该行为在交通流中的传播。

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引用次数: 0

Investigating the dynamics of speed and acceleration at merging and diverging sections using UAV based trajectory data 基于无人机轨迹数据的合并和发散路段速度和加速度动力学研究

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.08.007

The present study evaluates the speed and acceleration characteristics at the merging and diverging sections near two toll plazas located on National Highway under mixed traffic conditions using trajectory data obtained from video recorded using unmanned aerial vehicles (UAVs). The whole study section of 280 m is divided into zones of 20 m each, and the speed-distance and acceleration-distance relations are studied. The study analyzes the speed variations among vehicle classes in merging and diverging sections. The study shows that due to heterogeneous traffic and weak lane discipline, the speed distribution deviates from the normal distribution and follows the generalized extreme value (GEV) distribution in merging and diverging sections. The average maximum lateral speed is 3.0 km/h in the diverging section and 8.0 km/h in the diverging section (2.6 times higher than in the diverging section). The overall lane selection and lane changes are only prominent in the range from 40 m to 160 m in the merging section and the range from 100 m to 200 m in the diverging section. The results of acceleration modeling indicate that most vehicle classes follow a parabolic profile, except two-wheelers (2Ws) and light commercial vehicles (LCVs), whereas cars follow a dual-regime model in the diverging section, which is consistent with previous literature. The study also identified critical speeds for each vehicle class in both the merging and diverging sections, which can be useful in designing toll plaza facilities and informing safety measures.

本研究利用无人驾驶飞行器（UAV）拍摄的视频获得的轨迹数据，评估了混合交通条件下国道两个收费站附近合流和分流路段的速度和加速度特征。整个 280 米的研究路段被划分为每个 20 米的区域，并对速度-距离和加速度-距离关系进行了研究。研究分析了合流和分流路段中不同级别车辆的速度变化。研究结果表明，由于交通异质性和车道规则性较弱，并线和分流路段的速度分布偏离了正态分布，遵循广义极值（GEV）分布。分流路段的平均最大横向速度为 3.0 公里/小时，分流路段的平均最大横向速度为 8.0 公里/小时（比分流路段高 2.6 倍）。整体的车道选择和车道变更仅在并线段的 40 米至 160 米范围内和分流段的 100 米至 200 米范围内比较突出。加速度建模结果表明，除两轮车（2W）和轻型商用车（LCV）外，大多数车辆类别都遵循抛物线曲线，而小汽车在分流路段遵循双时态模型，这与之前的文献一致。该研究还确定了每个车辆类别在合流和分流路段的临界速度，这对设计收费广场设施和采取安全措施非常有用。

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引用次数: 0

Measuring students’ satisfaction levels for transit services: An application of latent class analysis 衡量学生对公交服务的满意程度：潜类分析法的应用

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.10.004

Past studies have identified the general public’s level of satisfaction with the service attributes of conventional fixed-route transit and ridesharing services, but few have limited their focus to students. This study employs latent class cluster analysis (LCCA) to identify clusters of university students, based on their satisfaction levels of the attributes of conventional fixed-route and ridesharing services, and uses a latent class behavioral model of a sample of university students in Arlington, Texas to explore the heterogeneity of their preferences toward ridesharing services. The results indicate that younger- and lower-income populations are more likely to be satisfied with on-demand ridesharing services than older- and higher-income populations, females are more likely to be satisfied with ridesharing services than males, and domestic students are more likely to be satisfied with ridesharing services than international students. The outcomes of the study will provide transportation planners with new insights about the significance of sociodemographic factors on the satisfaction level of those who use conventional transit and on-demand ridesharing services and will help them incorporate strategies that will make their services more attractive to their potential ridership.

以往的研究已经确定了普通公众对传统固定路线公交和共享乘车服务属性的满意度，但很少有研究将重点放在学生身上。本研究采用潜类聚类分析（LCCA），根据大学生对传统固定路线公交和共享乘车服务属性的满意程度，识别出大学生聚类，并使用德克萨斯州阿灵顿市大学生样本的潜类行为模型，探讨他们对共享乘车服务偏好的异质性。研究结果表明，年轻和低收入人群比年长和高收入人群更容易对按需共享乘车服务感到满意，女性比男性更容易对共享乘车服务感到满意，国内学生比留学生更容易对共享乘车服务感到满意。研究结果将为交通规划者提供新的见解，让他们了解社会人口因素对使用传统公交和按需共享乘车服务的人的满意度的重要影响，并帮助他们制定策略，使其服务对潜在乘客更具吸引力。

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引用次数: 0

A probabilistic reasoning approach to analyze the severity of single-vehicle crashes at mid-ramp locations 分析匝道中段单车碰撞严重程度的概率推理方法

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.10.002

Freeway ramps are one of the roadway elements that are considered as crash-prone sites with relatively more crashes per mile than other freeway segments. Among other crash types that occurred on freeway ramps, single-vehicle crashes have been found to be more severe. Thus, understanding the factors influencing the severity of single-vehicle crashes on freeway ramps is essential in improving the safety of our limited-access facilities. This study adopted a discrete Bayesian network (BN) approach to explore the probabilistic relationship among the potential factors associated with the severity of single-vehicle crashes at mid-ramp locations. The analysis was based on 6 041 single-vehicle crashes that occurred at the mid-ramp locations in California from 2009 to 2017. The findings indicated that ramp type, ramp traffic volume, road surface condition, and time of day were directly associated with the severity of single-vehicle crashes at the mid-ramp locations. The interdependency of off-ramps, ramp AADT of less than 13 000 vehicles per day, dry road surface condition, and off-peak hours were associated with the highest risk of fatal/severe injury crashes involving a single-vehicle. The study findings could potentially be used by transportation agencies in planning and implementing several strategies to improve the safety of freeway ramps.

与其他高速公路路段相比，高速公路匝道是每英里碰撞事故相对较多的易发路段之一。在高速公路坡道上发生的其他撞车类型中，单车撞车事故更为严重。因此，了解影响高速公路匝道上单车碰撞严重程度的因素对于提高我们有限通行设施的安全性至关重要。本研究采用离散贝叶斯网络（BN）方法探讨了与匝道中间位置单车碰撞严重程度相关的潜在因素之间的概率关系。分析基于 2009 年至 2017 年在加利福尼亚州中间匝道位置发生的 6 041 起单车碰撞事故。研究结果表明，匝道类型、匝道交通量、路面状况和一天中的时间与中间匝道位置单车碰撞事故的严重程度直接相关。非匝道的相互依存性、匝道每日平均车流量少于 13 000 辆车、干燥的路面状况以及非高峰时段与涉及单车的致命/重伤交通事故的最高风险相关。交通机构在规划和实施改善高速公路匝道安全的若干策略时，有可能会用到这些研究结果。

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引用次数: 0

Single-vehicle roadway departure crashes at rural two-lane highway curved segments: A diagnosis using pattern recognition 农村双车道高速公路弯道段的单车道偏离事故：利用模式识别进行诊断

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.10.005

Curved segments account for a disproportionately high proportion of fatal and serious injury crashes, with most of these crashes occurring on rural two-lane (R2L) highways. During the 10-year period from 2008 to 2017, a total of 1 234 fatal single-vehicle roadway departure (SV-RwD) crashes occurred on R2L roads in Louisiana, out of which 635 (51.5 %) crashes occurred on curved segments. Therefore, it is critical to investigate the causes of SV-RwD crashes, specifically those that occur on curved segments. This study aimed to investigate the ‘association knowledge’ of the factors contributing to SV-RwD crashes on R2L curved segments in Louisiana using fatal and injury crash data collected from 2008 to 2017. The study utilized Cluster Correspondence Analysis (CCA), a robust joint dimension reduction and clustering method for handling high-dimensionality and multicollinearity of crash data, to achieve this objective. Based on the cluster validation measures, the study identified five clusters with specific traits, including alcohol-impaired male drivers with no seatbelt usage, young (15–24 years old) female drivers’ crash involvement in cloudy weather conditions, animal-involved crashes in rainy weather conditions, crashes occurring on hillcrest locations under cloudy weather conditions, and crashes in the dark with the presence of streetlights and higher traffic volume. Furthermore, young (15–24 years) female drivers were identified in most clusters, implying that this specific age group of female drivers requires special consideration when dealing with SV-RwD collisions on R2L curved segments. To improve safety on R2L curved segments, policymakers can use the findings of this study to develop targeted countermeasures.

在致命和重伤交通事故中，弯道路段所占比例过高，而这些交通事故大多发生在农村双车道（R2L）公路上。在 2008 年至 2017 年的 10 年间，路易斯安那州的 R2L 公路上共发生了 1 234 起致命的单车道路偏离（SV-RwD）事故，其中 635 起（51.5%）发生在弯道路段。因此，调查 SV-RwD 事故的原因，特别是发生在弯道上的 SV-RwD 事故的原因至关重要。本研究旨在利用 2008 年至 2017 年收集的致命和受伤碰撞数据，调查导致路易斯安那州 R2L 曲线路段 SV-RwD 碰撞事故的因素的 "关联知识"。为实现这一目标，研究采用了聚类对应分析法（CCA），这是一种稳健的联合降维和聚类方法，用于处理碰撞数据的高维性和多重共线性。根据聚类验证措施，研究确定了五个具有特定特征的聚类，包括酒精受损且未使用安全带的男性驾驶员、在阴天条件下发生碰撞事故的年轻（15-24 岁）女性驾驶员、在雨天条件下发生的涉及动物的碰撞事故、在阴天条件下发生在山顶位置的碰撞事故，以及在有路灯且车流量较大的黑暗条件下发生的碰撞事故。此外，大多数群组中都发现了年轻（15-24 岁）的女性驾驶员，这意味着在处理 R2L 弯道上的 SV-RwD 碰撞事故时，需要特别考虑这一特定年龄段的女性驾驶员。为改善 R2L 弯道路段的安全状况，决策者可利用本研究结果制定有针对性的对策。

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引用次数: 0

Comparison of models with and without roadway features to estimate annual average daily traffic at non-coverage locations 比较有和无道路特征的模型，以估算非覆盖地点的年平均日交通量

IF 4.3 Q2 TRANSPORTATION

International Journal of Transportation Science and Technology

Pub Date : 2024-09-01 DOI: 10.1016/j.ijtst.2023.10.001

This study develops and evaluates models to estimate annual average daily traffic (AADT) at non-coverage or out-of-network locations. The non-coverage locations are those where counts are performed very infrequently, but an up-to-date and accurate estimate is needed by state departments of transportation. Two types of models are developed, one is that simply uses the nearby known AADT to provide an estimate, the other is that requires roadway features (e.g., type of median, presence of left-turn lane). The advantage of the former type is that no additional data collection is needed, thereby saving time and money for state highway agencies. A natural question that this study seeks to answer is: can this type of model provide equally as good or better estimates than the latter type? The models developed belonging to the first type include hybrid-kriging and Gaussian process regression GPR model (GPR-no-feature), and the models developed belonging to the second type include point-based model, ordinary regression model, quantile regression model, and GPR model (GPR-with-features). The performance of these models is compared against one another using South Carolina data from 2019 to 2021. The results indicate that the GPR-with-features model yields the lowest root mean squared error (RMSE) and lowest mean absolute percentage error (MAPE). It outperforms the hybrid-kriging model by 6.45% in RMSE, GPR without features model by 4.25%, point-based model by 4.69%, regular regression model by 11.35%, and quantile regression model by 4.25%. Similarly, the GPR-with-features model outperforms the hybrid-kriging model by 25.21% in MAPE, GPR without features model by 17.81%, point-based model by 22.26%, regular regression model by 26.36%, and quantile regression model by 21.07%.

本研究开发并评估了用于估算非覆盖或网络外地点的年平均日交通量 (AADT) 的模型。非覆盖地点是指那些不经常进行统计，但各州交通部门需要最新、准确估计的地点。我们开发了两种模型，一种是简单地使用附近已知的平均车流量来提供估计值，另一种是需要道路特征（如中间分隔带的类型、左转车道的存在）。前者的优点是不需要额外的数据收集，从而为国家公路机构节省了时间和金钱。本研究试图回答的一个自然问题是：这种类型的模型能否提供与后一种类型同样好或更好的估计结果？属于第一种类型的模型包括混合克里金法和高斯过程回归 GPR 模型（无特征 GPR），属于第二种类型的模型包括基于点的模型、普通回归模型、量子回归模型和有特征 GPR 模型（有特征 GPR）。利用南卡罗来纳州 2019 年至 2021 年的数据对这些模型的性能进行了比较。结果表明，带特征的 GPR 模型产生的均方根误差（RMSE）和平均绝对百分比误差（MAPE）最小。它的 RMSE 值比混合克里金模型高出 6.45%，比无特征 GPR 模型高出 4.25%，比基于点的模型高出 4.69%，比常规回归模型高出 11.35%，比量化回归模型高出 4.25%。同样，带特征的 GPR 模型的 MAPE 值比混合克里金模型高出 25.21%，不带特征的 GPR 模型高出 17.81%，基于点的模型高出 22.26%，常规回归模型高出 26.36%，量化回归模型高出 21.07%。

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引用次数: 0