{"title":"A large-scale analytical residential parcel delivery model evaluating greenhouse gas emissions, COVID-19 impact, and cargo bikes","authors":"","doi":"10.1016/j.ijtst.2023.08.002","DOIUrl":"10.1016/j.ijtst.2023.08.002","url":null,"abstract":"<div><div>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 <em>R</em><sup>2</sup> 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.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 136-154"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45905188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Risk analysis of bridge maintenance accidents: A two-stage LEC method and Bayesian network approach","authors":"","doi":"10.1016/j.ijtst.2023.07.002","DOIUrl":"10.1016/j.ijtst.2023.07.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 51-64"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44619645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Mining motif periodic frequent travel patterns of individual metro passengers considering uncertain disturbances","authors":"","doi":"10.1016/j.ijtst.2023.07.005","DOIUrl":"10.1016/j.ijtst.2023.07.005","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 102-121"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49121124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Evaluation of false alarm alarms in truck FCW based on calibration of RSS model under different driving scenarios","authors":"","doi":"10.1016/j.ijtst.2023.07.001","DOIUrl":"10.1016/j.ijtst.2023.07.001","url":null,"abstract":"<div><div>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 <em>k</em>-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.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 35-50"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54865107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Are current microscopic traffic models capable of generating jerk profile consistent with real world observations?","authors":"","doi":"10.1016/j.ijtst.2023.08.008","DOIUrl":"10.1016/j.ijtst.2023.08.008","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 226-243"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Investigating the dynamics of speed and acceleration at merging and diverging sections using UAV based trajectory data","authors":"","doi":"10.1016/j.ijtst.2023.08.007","DOIUrl":"10.1016/j.ijtst.2023.08.007","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 211-225"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42653322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Measuring students’ satisfaction levels for transit services: An application of latent class analysis","authors":"","doi":"10.1016/j.ijtst.2023.10.004","DOIUrl":"10.1016/j.ijtst.2023.10.004","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 284-297"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135850208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A probabilistic reasoning approach to analyze the severity of single-vehicle crashes at mid-ramp locations","authors":"","doi":"10.1016/j.ijtst.2023.10.002","DOIUrl":"10.1016/j.ijtst.2023.10.002","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 260-270"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135761680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Comparison of models with and without roadway features to estimate annual average daily traffic at non-coverage locations","authors":"","doi":"10.1016/j.ijtst.2023.10.001","DOIUrl":"10.1016/j.ijtst.2023.10.001","url":null,"abstract":"<div><div>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%.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 244-259"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135761676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Single-vehicle roadway departure crashes at rural two-lane highway curved segments: A diagnosis using pattern recognition","authors":"","doi":"10.1016/j.ijtst.2023.10.005","DOIUrl":"10.1016/j.ijtst.2023.10.005","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":52282,"journal":{"name":"International Journal of Transportation Science and Technology","volume":"15 ","pages":"Pages 298-318"},"PeriodicalIF":4.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135849512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}