Journal of Transportation Systems

Determination of Dynamic Passenger Car Unit for Heterogeneous Traffic: A Case Study of a Multi-Lane Divided Highway in Nepal

Rajesh Khadka — 2026-10-28

Nepal’s traffic is characterized by highly heterogeneous, non-lane-based traffic, where different vehicle categories interact with the same roadway space. The use of constant Passenger Car Unit (PCU) values under such conditions may not adequately represent the dynamic effects of vehicle characteristics and prevailing traffic conditions. This study determines Dynamic Passenger Car Unit (DPCU) values for major vehicle categories under heterogeneous traffic conditions. The study selected a 54m straight mid-block section of the four-lane divided Araniko Highway between Sallaghari and Chundevi. Videographic surveys were conducted over four days: three consecutive days and an independent day two weeks later, with observations during morning, afternoon, and evening periods. DPCU values were estimated using the Modified Homogenization Coefficient Method, considering the relative operating speed and roadway space occupancy of each vehicle category with respect to passenger cars. Temporal stability was assessed using the coefficient of variation, while independent validation was performed using the Absolute Percentage error. The final DPCU values were 0.23 for two-wheelers, 1.12 for utility vehicles, 1.47 for mini trucks, 2.98 for buses, and 3.01 for trucks, with passenger cars as the reference vehicle (DPCU=1.00). The proposed values can support traffic volume conversion, capacity analysis, and highway planning and design for multi-lane divided highways under similar conditions.

Crash Severity Prediction for Road Safety Policy: Random Forest Analysis in Bangladesh

Antora Roy — 2026-10-19

Road traffic accidents kill over 12,000 Bangladeshis annually, costing 2% of gross domestic product. This study develops a machine learning classifier for accident severity prediction using 750 crashes from Khulna, Bangladesh. A Random Forest algorithm trained on driver demographics, environmental conditions, and crash dynamics achieved 82.1% accuracy and 75% fatal-outcome recall on held-out test data, substantially exceeding Logistic Regression (71.3%) and Support Vector Machine (78.0%). Feature importance analysis revealed speed (16.2%) and major contributing cause (14.4%) as dominant predictors, substantially outranking licensing status (3.9%) and gender (0.9%), reflecting developing-country accident characteristics diverging from developed-country models. Stratified 80/20 train-test splitting with SMOTE-based class imbalance handling addressed the overrepresentation of non-fatal outcomes (78.7%) relative to fatal cases (21.3%). Cross-validation (5-fold stratified) confirmed generalization (79.8% ± 2.3% accuracy). The model's 81.2% fatal prediction precision enables evidence-based emergency resource deployment. Key finding: speed enforcement represents the dominant modifiable risk factor in Bangladeshi traffic contexts, supporting government policy prioritization. The study provides empirical validation for South Asian-specific transportation safety interventions and establishes a methodology for developing-country accident severity prediction adapted to local traffic environments, vehicle type distributions, and causation patterns distinct from developed economies.

Predicting Serious Injury in ADAS-Involved Crashes: A Comparative Machine Learning Analysis of NHTSA Standing General Order Reports, 2021-2025

Antora Roy — 2026-09-07

Advanced Driver Assistance Systems (ADAS) are now standard equipment on most new vehicles, yet what happens to occupants once a crash does occur remains thinly documented outside simulation studies. This study models injury severity using crash reports filed under the National Highway Traffic Safety Administration (NHTSA) Standing General Order, covering January 2021 through June 2025. Records with unknown outcomes were dropped and duplicate filings consolidated, leaving 496 crashes. Severity was treated as binary: serious or fatal injury versus everything else. Twenty raw variables spanning environment, collision configuration, restraint deployment, vehicle attributes, and pre-crash motion were expanded to 80 encoded features, and five classifiers were compared under identical splits. Random Forest performed best, reaching 86.0% accuracy with an AUC-ROC of 0.859; five-fold cross-validation returned 84.6% ± 3.3%. Recall on the serious class was only 45.0%, however, meaning the model missed more than half of the severe outcomes it was built to catch. Manufacturer identity (Tesla, 15.26%) and model year (7.86%) outranked airbag status (11.04%) and collision configuration (8.86%) in the importance ranking. For screening use, the decision threshold must be lowered well below 0.5, trading precision for the sensitivity such applications require.

Disaster-Resilient Transportation Corridors for Tourism and Trade Development in Nepal: A Comparative Assessment of Hyperloop, High-Speed Railway, and High-Speed Highway

Anuj Kunwar — 2026-10-19

Nepal’s rugged terrain and frequent landslides, floods, and earthquakes pose significant challenges to transport connectivity, reliability, tourism, trade, and disaster response. This study presents a conceptual comparative assessment of hyperloop, high-speed railway, and high-speed highway (expressway) across three proposed trans-Himalayan corridors: Gorakhpur-Lumbini-Swargadwari-Pokhara-Kathmandu, Patna-Birgunj-Nijgadh-Kathmandu, and Kerung-Kathmandu-Everest Base Camp. The study integrates secondary data, Google Earth/GIS-based elevation analysis, and published cost and operating-speed benchmarks to estimate route lengths, tunnel and bridge requirements, construction costs, and travel times. The proposed three corridors span approximately 439 km, 260.8 km, and 204 km, respectively, requiring 28 tunnels and 313 bridges in total. Across the three corridors, estimated capital costs were USD 9.50-26.10 billion for hyperloop, USD 4.28-9.22 billion for High-Speed Railway, and USD 3.87-8.34 billion for high-speed highway. Corresponding travel times ranged from 20.40-43.90 min for hyperloop, 34.97-75.00 min for High-Speed Railway, and 122-263 min for high-speed highway. Hyperloop offers the fastest travel times but requires substantially higher investment and remains subject to technological and implementation uncertainties. High-speed highway provides the lowest initial cost, whereas high-speed railway offers a comparatively balanced combination of cost, travel time, and technological maturity. The findings provide a preliminary basis for resilient transport investment in Nepal, supporting phased development of expressways and high-speed railways in the near term while positioning hyperloop as a potential longer-term option as its technological maturity advances.

Road Safety: Driving Habits and Defensive Driving-A Comprehensive Analysis

Sadagoban Kalidass — 2026-09-08

Road traffic fatalities remain a critical socio-economic crisis, driven by individual behavioral failures and inadequate driver perception rather than a lack of statutory regulations. Conventional enforcement models intervene post-licensing, failing to restructure embedded, subconscious reflexes formed during early development. This paper establishes a holistic road safety model integrating human ethics, spatial mechanics, and institutional curriculum reform. "Observe, Monitor, Predict" (OMP) cognitive model for defensive driving, which trains drivers to continuously evaluate environmental variables, eliminate blind-spot risks, and project a 100-foot visual target path to mitigate panic braking. Furthermore, the proposal demonstrates how personal ethical traits, such as capacity awareness, situational yielding, and payload responsibility, directly mirror vehicle control mechanics. To turn these principles into lifelong reflexes, this policy proposal advocates for mandatory bicycle-based traffic education across Grades 1 to 8, utilizing "Children’s Traffic Parks" supervised by certified Physical Education Teachers. By aligning school-level practical training with statutory licensing standards (8th-standard qualification at age 18), this strategy transforms road safety from a reactive legal mandate into an internalized cultural responsibility, safeguarding human lives and national productivity.