Aug 2026· Future Transportation· Vol 6, pp. 178· 0 citations· 54 references
Abstract
This study investigates the network-level traffic and safety effects of increasing Market Penetration Rate (MPR) of SAE Level 2 and Level 3 automated vehicles under take-over conditions. A calibrated microsimulation model of a real-world 50 km highway corridor in Greece (Nea Odos) was used to develop 18 scenarios combining two take-over contexts (Lane Closure and ODD Exit), two Time Budget (TB) configurations, and four MPR levels (25–100%), complemented by two baselines. Traffic performance was assessed through five network-level indicators (speed, delay, lane-changing frequency, travel time, and density). Safety was evaluated through 674 conflict events extracted via the Surrogate Safety Assessment Model (SSAM), using Time-to-Collision (TTC) as the primary indicator. MPR is the dominant feature of both dimensions. Increasing MPR produces a monotonic reduction in speed (−19.2% at 100% MPR), a sharp decline in lane-changing (−59.8%), and increases in density (+21.1%) and travel time (+22.4%), delay peaks non-monotonically at 50% MPR (+107.9%). Spearman correlations between MPR and the traffic indicators are very strong for speed, lane changes, travel time, and density, while delay time yields a weaker correlation reflecting its non-monotonic response. All operating points remain within free-flow conditions throughout the MPR range. The MPR effect on TTC is marginal (p = 0.065) but non-monotonic, with 75% MPR yielding significantly higher mean TTC than 50% and 100% MPR. Neither TB duration nor TOR context produces a statistically significant effect on traffic indicators or TTC distributions, with negligible effect sizes in both cases. Traffic costs of mixed-traffic automation appear at partial penetration, while network-level safety benefits remain limited and non-linear.
SUMO (Simulation of Urban MObility) provides considerable capability for traffic studies in developing countries. However, by default it is calibrated for lane-disciplined, homogeneous traffic of Europe. This study developed an aggregate level calibration strategy to make SUMO suitable for heterogeneous traffic in signalized intersections. In this regard, two traffic signalized intersections in Sri Lanka, namely Piliyandala (four-legged) and Katubedda (three-legged), were studied through detailed geometric and signal timing information. Traffic data collected through videos on site comprised vehicular classification, discharge rates, and queue lengths in each approach for each signal cycle, which were then aggregated at 15-minute intervals. Nine vehicle types corresponding to local traffic mixture were introduced, along with iterative calibration of behavioral parameters to match observed traffic operation. Model validity in Piliyandala was evaluated using the mean absolute percentage error (MAPE) and Geoffrey E. Havers (GEH) statistic, with results indicating satisfactory agreement for classified traffic discharge counts and queue lengths, as reflected by MAPE values below 15% and GEH values below 5, with minor exceptions in complex shared-lane queues. Once satisfied, the same SUMO parameter set was used in Katubedda to forecast the flow and the queue length. Results show that in Katubedda, statistical tests satisfy the accepted thresholds without further calibration. This establishes the model transferability of SUMO in various intersections in areas with similar traffic patterns without resource intensive individual calibration.
S.A.S.T. Salawavidana, H. Pasindu, J.M.S.J. Bandara et al.· Proceedings of the 19 th Tra...· 0 citations
Improving emergency vehicle mobility in congested urban environments is a critical challenge for transportation systems. Although roadway capacity expansions, such as widening roads, are often deployed to reduce congestion, their impact on emergency response performance is not always guaranteed, especially when delays concentrate at critical intersections. This study investigates how roadway capacity expansion affects emergency vehicle performance by using a microscopic traffic simulation framework. The study was applied to a real urban corridor in Mohammedia, Morocco, to provide a solid base for simulations with real-world conditions. A SUMO model was calibrated to represent two roadway configurations: a baseline two-lane layout and a three-lane post-widening scenario. Traffic volumes from 1056 to 3520 vehicles per hour were simulated, and performance was assessed using three emergency-specific indicators: Emergency Response Time (ERT), Delay Ratio (DR), and Priority Mobility Index (PMI). An initial single-run comparison suggested a substantial ERT reduction under moderate demand (343.40 s to 270.90 s, 21.11%); however, a 30-seed replication with paired Wilcoxon signed-rank tests shows that this and nearly all other widening effects are not statistically distinguishable from stochastic simulation noise. Only one of 12 emergency vehicle comparisons (Priority Mobility Index at 18:00) reached significance, and it favored the baseline configuration; none of 12 general traffic comparisons improved significantly, and general traffic was significantly slower under the widened configuration at 22:00 (p < 0.01). A supplementary sensitivity analysis (±20% emergency vehicle demand share) further shows that Delay Ratio conclusions are considerably more sensitive to this assumption (up to 34% relative change) than ERT or PMI (under 8%). These findings indicate that, in this network, roadway capacity expansion alone does not deliver a statistically robust improvement in either emergency vehicle or general mobility, and that a persistent signalized-intersection bottleneck remains the dominant constraint irrespective of lane geometry. The study provides a replicable, statistically validated simulation framework for assessing roadway capacity expansion effectiveness and cautions against single-run comparisons, which can substantially overstate the causal effect of infrastructure interventions in microscopic traffic simulation studies.
Imane Chakir, Mohamed El Khaili, Adil El Arfaoui et al.· Future Transportation· 0 citations
A demand-driven signal control strategy is developed to allocate green time based on real-time vehicle demand, eliminating wasted signal phases and providing a scalable and intelligent solution for modern smart city traffic systems.
Friday Idakwo David, S. T. Apeh, Oduware Okosun· E3S Web of Conferences· 0 citations
The introduction of Connected and Autonomous Vehicles (CAVs) into the existing traffic system represents one of the greatest challenges of modern road traffic engineering. Beyond their role as active traffic participants, CAVs can also be regarded as mobile (floating) sensors, effectively turning the vehicle fleet itself into a distributed, city-wide and motorway-wide sensing infrastructure. The transition from fully human-driven vehicles to fully autonomous vehicles will take decades, giving rise to a prolonged mixed-traffic period in which vehicles with different levels of automation share the same road space. This paper analyses the parameters and measures used for evaluating the throughput, environmental impact, and safety of traffic networks at different CAV penetration rates. It further reviews studies that rely exclusively on data collected from CAVs acting as mobile sensors, examining data-aggregation and traffic-state-estimation methods used to reconstruct macroscopic traffic parameters such as flow, density, headway, and speed. Additionally, measures for evaluating specific use cases for CAVs including mobility-on-demand services and their cost comparison with human-driven taxi operations are also addressed. The energy and emissions implications of CAV deployment, including the added burden of sensing hardware and system-level rebound effects, are also examined. Based on the synthesis performed, a set of representative CAVs penetration rates is proposed as a standardised framework for future mixed-traffic flow evaluations.
Lucija Bukvić, M. Gregurić, Filip Vrbanić et al.· Vehicles· 0 citations
This paper presents a microscopic simulation modeling of 30 signalized intersections in Riyadh, using detailed datasets on direction traffic volume, lane configurations, and current signal control structure. Six distinct Median U-Turn scenarios were developed, categorized by longitudinal offset from the intersection center ranging from 120–300 meters. These configurations were closely evaluated against the conventional signalized intersection for operational viability. Simulations were executed within PTV VISSIM for both the baseline signalized control and the six MUT alternatives, resulting in more than 3,000 simulation runs to ensure statistical soundness in extracting travel time, delay, and queue length metrics. The final results indicate that the MUT design outperforms the conventional signalized intersection at total entering volumes (TEV) of 4,000 vehicles per hour (vph) or less. Within this range, the results identify optimal offsets of 150m for moderate volumes, 180–210m for high directional splits, and 250–300m for high-intensity demands (>1,200 vph). While MUT designs excel below 4,000 vph, the conventional four-leg signalized intersections consistently emerge as the superior design once this total volume threshold is exceeded.
Abdulrahman Badwes, Saif Alarifi· PLoS ONE· 0 citations
Developing countries, including Indonesia, are characterized by Heterogeneous Disordered Traffic (HDT), making traditional technology adoption models for autonomous vehicles (AVs) unsuitable. To bridge this gap, this study surveyed 204 urban early adopters using a rigorous psycho-statistical framework. Utilizing t-tests, ANOVA, and bootstrapped ordinal regression, we validated three behavioral pillars: safety-first prioritization, educated critical adoption, and a direct mode shift to shared AVs. Subsequent market segmentation via a CHAID decision tree yielded empirical AV market penetration rates (MPR) of 20%, 40%, and 70%. MPRs were evaluated in a calibrated PTV VISSIM microsimulation of the A.P. Pettarani Arterial Road in Makassar, Indonesia. A tree-based ensemble framework (XGBoost, CatBoost, and Random Forest) with SHAP diagnostics and a Double Machine Learning causal protocol assessed infrastructure trade-offs. With an R2 of 0.99 and MAPE of 9.9%, XGBoost strongly predicted intersection delays and road section speeds. Causal analysis shows that putting dedicated lanes in place too soon, when the penetration rate is low (≤20% MPR), makes traffic worse. At an intermediate 40% MPR, dedicated lanes reduce intersection bottlenecks. Mixed non-dedicated traffic wins at 70% widespread saturation. The AV fleet acts as macroscopic pacemakers, smoothing traffic shockwaves and reducing intra-lane oversaturation to maximize global network efficiency without spatial segregation.
M. Ahnan, Dukgeun Yun· Applied Sciences· 0 citations