Search Results (44)

Cycling is increasingly promoted as part of sustainable transport strategies, but safety concerns remain an important barrier. Crash-based safety assessment is limited because crashes are reactive indicators and cycling crashes are often underreported. This study evaluates whether smartphone inertial sensor data can be used to detect selected potentially critical cycling manoeuvres that may support proactive safety screening. A two-stage workflow intended for possible future smartphone implementation was evaluated offline. In Stage 1, kinematic thresholds were applied to identify short candidate sensor windows when a high-dynamic manoeuvre was detected. In Stage 2, a machine learning classifier was applied to these candidate windows to classify the manoeuvre type and reduce false positives. The workflow was evaluated using three datasets collected in Germany from 10 riders, including controlled test-track manoeuvres and urban rides in Aachen and Bonn. The dataset included 148 hard-braking and 132 abrupt-swerving manoeuvres. In addition, 45 bicycle-only crash-like reference sequences were collected to compare low-speed impact-like signal patterns. In leave-one-dataset-out evaluation, the Support Vector Machine achieved mean precision–recall areas under the curve of 0.954 for hard braking and 0.884 for abrupt swerving. The corresponding mean F1-scores were 0.924 and 0.876. The offline evaluation showed that smartphone sensor data can support the detection of selected high-dynamic cycling manoeuvres with good classification performance under controlled and semi-controlled conditions. However, most target manoeuvres were controlled or rider-initiated proxy manoeuvres rather than confirmed traffic conflicts. The results should therefore be interpreted as manoeuvre detection, not as direct crash-risk validation. In future naturalistic studies, the workflow could help identify candidate locations where cyclists frequently brake hard or swerve abruptly. These locations can then be prioritised for site inspections, video-based conflict analysis, or comparison with crash records. Full article

The growing deployment of right-turn warning zones (RTWZs) at urban intersections in China aims to enhance the operational safety of urban traffic systems, yet encroachment upon these zones by pedestrians and non-motorized vehicle (NMV) users remains a practical concern. This study combined a field observation at two intersections in Hefei, China, to examine these vulnerable road users’ actual unsafe RTWZ encroachment behaviors, with an extended theory of planned behavior (TPB) questionnaire survey to explore the cognitive mechanisms underlying intentions of such encroachment. Observational results showed that NMV riders encroached on RTWZs significantly more frequently than pedestrians, and such encroachments occurred more often during morning peak hours. The questionnaire survey on 402 respondents revealed that attitude, perceived behavioral control (PBC), and past behavior showed the strongest positive effect on encroachment intention, both directly and indirectly through PBC. In contrast, perceived risk and safety knowledge negatively predicted intention indirectly, while also showing significant indirect effects on intention impacting attitude. Subjective norm, sociodemographic characteristics, and travel patterns were not significant predictors. These findings not only provide a deeper understanding of the real-world effectiveness of RTWZs, but also offer an evidence-based basis for developing countermeasures to improve operational safety of urban slow traffic systems. Full article

Few national comparisons exist on healthcare utilization among e-scooter users, and prior research was limited by the absence of e-scooter-specific ICD-10-CM codes before 2021. Using newly implemented codes, this cross-sectional study compared emergency department disposition and injury patterns between e-scooter riders and bicyclists using the 2021 Nationwide Emergency Department Sample (n = 239,457 weighted encounters). Multivariable logistic regression adjusted for demographic, geographic, and temporal confounders, with interaction terms for insurance status, substance use, and motor vehicle involvement. E-scooter riders were more often female (46.5% vs. 24.7%) and under age 18 (47.6% vs. 37.4%) than bicyclists. Overall adjusted odds of admission or transfer did not differ significantly between groups (OR = 1.03; 95% CI: 0.86–1.23). Motor vehicle involvement significantly modified this relationship, with e-scooter riders showing higher odds of admission (OR = 1.67; 95% CI: 1.12–2.48). No significant effect modification was detected for insurance status or substance use. E-scooter riders more frequently sustained distal extremity fractures consistent with low-energy falls, while bicyclists experienced more proximal fractures and internal trauma. Updated ICD-10-CM codes improve e-scooter injury surveillance, and motor vehicle involvement represents a priority target for mode-specific prevention and infrastructure investment. Full article

Motorcyclists are among the most vulnerable road users at roundabouts, particularly in mixed-traffic environments where rider behaviour may be influenced by geometric design. This study investigates the association between roundabout geometry and risky motorcyclist behaviour using the Chi-squared Automatic Interaction Detection (CHAID) method. Video-based observations were conducted at four selected roundabouts in Kuching, Sarawak, Malaysia, generating 15,937 risky-behaviour events from 5400 observed motorcyclists. Six risky-behaviour categories were analysed, covering entry, circulation, and exit manoeuvres. The CHAID results showed that entry radius was the primary geometric factor associated with risky motorcyclist behaviour, while exit radius and exit width acted as secondary variables under specific entry-radius conditions. Four behavioural scenarios were identified. Entry radii of 15–32 m were associated with mixed risky-behaviour patterns, with lane splitting during circulation being the most frequent behaviour. Entry radii of 37–40 m combined with exit radii ≤ 12.43 m were associated with failure to signal before exiting. Entry radii of 43–49 m combined with exit radii ≤ 25.3 m were associated with improper lane positioning when exiting. Larger entry radii of 49–64 m combined with exit widths of 7.38–9.06 m were associated with close stopping or potential blind-zone positioning. The model validation results indicated moderate internal classification performance, supporting the use of CHAID as an interpretable threshold-identification tool rather than a high-precision predictive model. The findings demonstrate that risky motorcyclist behaviour at roundabouts is shaped by non-linear interactions between entry and exit geometric elements. From a sustainability perspective, these results provide preliminary evidence for behaviour-sensitive roundabout design, safety assessment, and policy-oriented geometric improvements that support safer and more inclusive urban transport systems in motorcycle-dominant mixed-traffic contexts. Full article

Retaining bus users is a practical condition for sustainable urban mobility, but transport agencies need defensible passenger-centered measures to judge whether service experience is credible enough to support continued use. This study tests an exploratory measurement approach linking perceived bus service quality, satisfaction, perceived value, and loyalty, using urban bus-user data collected in Pakistan as the empirical setting rather than as a national-representative case. A mixed-mode survey produced 230 responses; 227 valid cases remained after pre-specified screening of ineligible and out-of-frame records. The analysis used item diagnostics, exploratory factor analysis, HC3-robust regression, bootstrapped indirect-association testing, subgroup description, and qualitative coding of open-ended responses. The retained sample was young, student-oriented, and strongly transit-dependent, so the results mainly describe regular bus users with limited modal flexibility. Service-quality items were factorable (KMO = 0.725; Bartlett’s chi-square = 851.56, df = 435, p < 0.001), but parallel analysis supported one dominant general service-quality signal rather than a stable five-dimensional SERVQUAL structure. General service quality (GSQ) is therefore treated as an empirical indicator of perceived service credibility, not as a validated new latent construct. GSQ was positively associated with satisfaction (beta = 0.382, p < 0.001), perceived value (beta = 0.389, p < 0.001), and loyalty after satisfaction and perceived value were included (beta = 0.233, p < 0.001). Bootstrap results were consistent with indirect association through satisfaction (estimate = 0.032, 95% CI [0.011, 0.057]) and perceived value (estimate = 0.035, 95% CI [0.010, 0.062]), while a residual direct association remained. Frequent riders rated reliability, responsiveness, perceived value, and loyalty more critically, but interaction testing did not confirm moderation by bus-use frequency. Open-ended comments repeatedly pointed to punctuality and passenger information, with safety also appearing as a salient threshold concern. The findings support passenger-centered sustainability monitoring by identifying service credibility, reliability, responsiveness, safety, and perceived value as practical indicators for retaining transit-dependent bus users, while ruling out any claim that the imported SERVQUAL structure was fully validated in this sample without confirmatory revalidation. Full article

Electric scooters (e-scooters) have rapidly integrated into university transportation networks; however, there is limited empirical understanding of users’ and non-users’ perceptions, which is essential for developing effective and inclusive policies. This study addresses this gap by analyzing the differential perceptions of e-scooter adoption, safety, and policy preferences at Louisiana State University (LSU). A quantitative, cross-sectional survey was administered to 1036 respondents (592 users and 444 non-users). Statistical analyses, including Chi-square tests and Binary Logistic Regression, were used to identify key perceptual differences and behavioral predictors of e-scooter usage. Results show that users were predominantly male undergraduates, with speed (90%) and convenience (61%) as the primary motivators. Users were over 12 times more likely to perceive e-scooters as safer than walking. In contrast, non-users cited frequent scooter misplacement (84%) as their top barrier to adoption. Logistic regression confirmed that concern about misplacement (Odds Ratio = 0.076) and support for restrictive policies were strong negative predictors of use, while belief in safety and low cost were positive predictors. These findings may help inform campus micromobility policy discussions. The strong negative perceptions associated with scooter misplacement suggest that designated parking hubs and geofencing strategies could help improve campus operations and pedestrian accessibility. In addition, because safety perception was identified as an important predictor of e-scooter use, targeted safety awareness and educational initiatives may help improve rider behavior and address perceived operational safety concerns. This strategy ensures a balance between user adoption incentives and the safety/accessibility needs of the entire university community. Full article

Aggressive riding behavior is a key contributing factor to road accidents, particularly in motorcycling, where rider dynamics directly influence vehicle stability and control. Despite growing interest in objective behavioral assessment, validated classification frameworks specific to motorcycles remain scarce in the literature. This pilot study investigated the feasibility of a standard deviation-based method for classifying aggressive riding behavior in a single experienced motorcyclist navigating two distinct environments: an urban route (UR) and a suburban national route (SNR). The participant completed two 20 min rides under real-world conditions. The UR was characterized by frequent accelerations, braking, speed bumps, and traffic lights, whereas the SNR features low traffic density and minimal interruptions. Longitudinal acceleration data were continuously recorded using a Vicon Blue Trident measurement unit mounted on the motorcycle seat. Drawing on the threshold principles established in automotive research, an environment-specific classification framework was developed to categorize riding events into normal, aggressive, and dangerous levels for both acceleration and deceleration maneuvers. The derived thresholds revealed pronounced environmental differences: UR thresholds (acceleration: 2.122 m/s²; deceleration: −2.134 m/s²) were approximately three times lower than those observed in the SNR (acceleration: 6.16 m/s²; deceleration: −7.09 m/s²). From more than four million recorded data points, approximately 88% of the riding behavior was classified as normal in both routes. In the UR, 9.27% of events were identified as aggressive and 4.37% as dangerous, compared with 7.27% aggressive and 5.35% dangerous events in the SNR. These preliminary findings suggest that environment-specific thresholds may be essential for accurately characterizing motorcycle riding behavior, and caution against the direct application of fixed automotive criteria to motorcycle safety analyses. All findings are specific to one rider on two routes and must not be extrapolated to other motorcyclists, vehicle types, or road contexts without replication. Full article

This study aimed to investigate how human, vehicle, and environment (HVE)-related factors and their interactions contribute to fall accidents related to electric scooters (e-scooters). Falls are the most common type of e-scooter accidents, and developing a thorough understanding of the factors that contribute to these accidents is critical for effective accident prevention. Unlike collisions, falls frequently result from the complex interaction among the rider, the vehicle, and the environment. To this end, this study conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines and uses the Haddon Matrix framework to identify and classify factors related to e-scooter fall accidents from HVE perspectives, spanning the pre-fall and fall phases. The findings suggest that e-scooter fall accidents are multifactorial, resulting from the interaction of HVE-related factors across accident phases rather than from a single cause. Human-related factors, vehicle attributes, and environmental conditions were all found to contribute to fall risk, with notable interactions identified across all three dimensions. This study contributes to a better understanding of the mechanisms underlying e-scooter fall accidents by systematically identifying these factors and examining their interactions, highlighting the need for further investigation into HVE interactions across diverse accident contexts. Full article

In equestrian circles, horse–rider harmony is understood intuitively, yet clear criteria are lacking. This study examined how different equestrians conceptualized and visually assessed harmony and how this influenced scoring. Qualitative interviews were combined with eye tracking technology. Thirty equestrians assessed five videos of horse–rider combinations performing in dressage, showjumping, eventing, working equitation and Icelandic riding, with eye movements being recorded using a mounted eye tracker. Verbal definitions were analyzed using thematic analysis, revealing three overarching themes: Horse Behavior, Horse–Rider Connection, and Rider Influence. Number of fixations and duration of fixation were reduced using principal component analysis (PCA), yielding five components each. These explained 70.9% and 64.5% of variance, respectively. Linear mixed-effects models showed significant effects for two PCA components: frequent fixation on the horse’s ears and eyes relative to the horse’s shoulder and rider leg predicted lower harmony scores (B = −0.34, SE = 0.13, z = −2.53, p < 0.05), whereas longer dwell time on the rider’s shoulder relative to the rider’s leg predicted higher scores (B = 0.25, SE = 0.12, z = 2.12, p < 0.05). Ears and eyes were also the most frequent first fixation. Harmony appears to be a shared construct at the conceptual level, but is personally enacted at the practical level. Equine facial expressions and rider posture serve as important perceptual indicators. Full article

Traffic accidents represent a significant threat to individuals, with motorcycles frequently involved. Despite concerted efforts by organizations like the World Health Organization and governments worldwide, reducing accident rates remains a challenge. Notably, Indonesia has witnessed a surge in traffic accidents, with motorcycles being a prominent mode of transport. This study aims to evaluate situational awareness and motorcycle riders’ behavior among Indonesians, with respect to factors such as riding time and age. This study involves laboratory-based research and uses quantitative primary data collected with the Situation Awareness Global Assessment Technique (SAGAT), the Situation Present Assessment Method (SPAM), and the Motorcycle Rider Behavior Questionnaire (MRBQ). The results indicate that overall situation awareness is low, with the lowest level among young riders. Nighttime situational awareness is also lower than during the daytime. Recommendations to improve situation awareness include periodic training with scenario-based sessions for motorcycle riders, strict adherence to driving regulations, the potential integration of motorcycle simulators, and prioritizing the program to enhance young riders’ situation awareness. These recommendations aim to boost rider safety and reduce motorcycle accidents. Full article

The recent expansion of mass public transit in Bangkok has increased demand for public motorcycle taxis as a first- and last-mile solution for sustainable urban mobility. This study presents the results of a real-world demonstration project that transitioned 50 conventional public motorcycle taxis into electric motorcycles supported by a battery-swapping system. The project evaluated vehicle performance, operational patterns, electricity consumption, and greenhouse gas (GHG) emissions under actual traffic conditions. Electric motorcycles deployed in taxi services must accommodate additional passenger load, provide sufficient acceleration for dense urban traffic, and sustain high daily travel distances. The findings show that participating riders accumulated a total driving distance of 759,354 km during the project period, demonstrating the technical and operational feasibility of electrification in high utilization fleets. Based on measured electricity consumption and Thailand’s grid emission factor, the transition resulted in an estimated reduction of approximately 1708.4 metric tons of CO₂ equivalent emissions, excluding additional benefits associated with modal shifts to mass public transit. The results further indicate that battery-swapping infrastructure is a critical operational enabler, as daily travel distances frequently exceed the single-charge range of typical electric motorcycles. Scenario projections aligned with Thailand’s 30-by-30 electric vehicle policy target suggest that large-scale electrification of motorcycle fleets could contribute substantially to national mitigation efforts, supporting the country’s accelerated goal of net-zero emissions target by 2050. Full article

Road transport across Asia is undergoing rapid motorisation and exemplifies growing road safety challenges, with rising accident rates closely linked to driver behaviour. Recent reports indicate that Vietnamese drivers often perceive risk as manageable and enforcement as inconsistent, contributing to habitual violations such as speeding, signal ignoring, and risky manoeuvres, particularly when traffic is light. Evidence shows that riders, especially young adults, feel confident controlling their vehicles and frequently disregard safety warnings. This study investigates traffic safety awareness among motorcyclists and car drivers in Hanoi, based on a questionnaire survey of 393 respondents. Principal Component Analysis (PCA) was used to group 11 attitudinal statements into key components influencing road safety perceptions, identifying five: non-compliance with traffic regulations (Component 1), aggressive driving behaviour (Component 2), traffic signal issues (Component 3), road quality and infrastructure (Component 4), and preventive measures (Component 5). Multiple Correspondence Analysis (MCA) and two-step cluster analysis (TCA) were then applied to determine user clusters by socio-demographic characteristics, producing three groups: young adults in employment riding motorcycles (Cluster 1), young adults in education riding motorcycles (Cluster 2), and mature adults in employment driving cars (Cluster 3). Finally, Multinomial Logistic Regression (MLR) was applied to assess variations in road safety perceptions across the different groups (clusters). Mature adults driving cars (Cluster 3) identified the first four components as significant, with Components 1 and 2 showing negative associations and Components 3 and 4 positive associations. Full article

In recent years, e-bikes have rapidly gained popularity in China. However, riders frequently engage in aberrant behaviors, posing significant traffic safety concerns. Field observation combined with traffic conflict techniques offer an effective approach for identifying risky riding behaviors that significantly affect traffic safety. This study aims to address two major limitations in existing research that can lead to estimation biases: the unsystematic and incomplete inclusion of potential risky riding behaviors, and the insufficient consideration of unobserved heterogeneity in conflict data. Data on 437 e-bike–motor vehicle conflicts were collected at four signalized intersections in Hefei, covering 21 variables including illegal, negligent, and error-prone riding behaviors, as well as sociodemographic factors. Appropriate conflict risk indicators were selected for straight-line and angle conflicts, respectively. A random parameters binary logit model with heterogeneity in means and variances (RPBL-HMV) was developed and compared against binary logistic and mixed logit models. The results indicate that the RPBL-HMV model provides a significantly better goodness-of-fit than the other two models. Six factors with fixed parameters are positively associated with high-risk conflicts, while two factors exhibit random parameters—one of which decreases in mean when riders fail to slow down before turning. The identified risky behaviors and the corresponding targeted countermeasures offer practical insights for regulating unsafe e-bike riding and improving intersection safety. Full article

This study focuses on the use of eye-tracking technology to analyse the rider’s visual attention during racing on a Ducati Panigale V2 motorcycle. Using the TOBII Pro Glasses 2 system, the rider’s gaze dynamics were recorded, including fixations, eye movements (saccades) and gaze distribution on key sections of the track. The results revealed a link between gaze stability and cornering efficiency, particularly in optimising braking points and selecting the ideal trajectory. Identifying unstable visual behavior—such as frequent gaze deviations or constant switching between reference points—provides valuable insights for improving driving technique. This approach confirms the importance of eye-tracking as a tool for objective evaluation and optimization of rider performance in motorsport. Full article

Frequent transit services in urban areas have the potential to increase their accessibility to transit-dependent riders and reduce congestion by attracting new ridership through a modal shift. However, bus services operating in mixed traffic face operational challenges that reduce reliability and hinder their attractiveness. The sources of unreliability can range from local-level conditions, like the road infrastructure, to higher-level decisions, like the service plan. For the effective planning of improvement strategies, both scales of analysis must be considered. This paper uses a novel modeling framework to understand reliability by analyzing the route and segment factors separately. The Chicago Transit Authority (CTA) bus network is used as a case study for the analysis. The data reflect the operational, demand, and urban conditions of 50 high-frequency bus routes. At the route level, we use the coefficient of headway variation as the dependent variable and diverse route characteristics as explanatory variables. The results indicate that the most significant contributors to the variability of headways are variability in schedules and dispatching at terminals. It is also found that driver experience impacts reliability and that east–west routes are more unreliable than north–south routes. At the segment level, we use data from trips involved in bunching and gaps. As the dependent variable, a novel measure is formulated to capture how quickly bunching or gaps are formed. The bunching and gap events are treated as separate regression models. Findings suggest that link and dwell time variability are the most significant contributors to gap and bunching formation. In terms of infrastructure, bus lane segments reduce gap formations, and left turns increase bunching and gap formations. The insights presented can inform improvements in service and transit infrastructure planning to improve transit level of service (LOS) and support the future of sustainable, smart cities. Full article