Research on advanced technology adoption has traditionally emphasised technology acceptance, behavioural intentions, and determinants of organisational adoption. Less attention has been given to adoption readiness, particularly in contexts where organisations have foundational digital capabilities yet cannot implement technologically complex innovations. This study investigates autonomous vehicle (AV) adoption readiness among logistics small and medium-sized enterprises (SMEs) in South Africa. It examines how operational realities and readiness conditions shape organisational assessments of implementation feasibility. The study uses a sequential explanatory mixed-methods design, combining survey data from 100 respondents representing logistics SMEs with 17 semi-structured interviews with owners, managers, and operational decision-makers. The findings indicate that logistics SMEs generally associate AV technologies with anticipated improvements in efficiency, safety, cost optimisation, operational resilience, and competitiveness. However, financial limitations, skills shortages, infrastructure deficiencies, regulatory uncertainty, security risks, and broader operational pressures still constrain adoption readiness. Existing digital technologies support operational optimisation but do not automatically provide the capabilities required for advanced automation. Readiness depends on aligning process, organisational, environmental, technological, and infrastructure conditions, with sociocultural influences shaping organisational assessments across these domains. The study distinguishes adoption readiness from technology acceptance, provides contextual empirical support for refining the Process–Organisation–Environment–Technology (POET) framework, and identifies the capability–complexity gap as an interpretive explanation for constrained implementation readiness.
Cooperative warning functions depend on whether V2X information becomes available in time to influence vehicle control. This article presents an event-based co-simulation framework coupling SUMO, ns-3 (ms-van3t), 5G-LENA NR sidelink and application records, with Sionna RT enabled for the intersection scenario. In 140 emergency-vehicle-warning runs, reducing mean packet reception ratio (PRR) from 0.9412 to 0.0675 accompanied an increase from 4.45 to 32.52 s in the median across runs of the within-run 90th percentile of first Cooperative Awareness Message (CAM) reception delay, measured from the sender’s first logged CAM transmission. At a fixed priority intersection, all endpoints were recomputed for the same nine complete-case runs per information mode, giving 27 runs. Median first controller-warning times were 2.068 s with high-quality V2X, 4.795 s with degraded V2X and 5.000 s with radar only. Collision records showed 0/9, 9/9 and 9/9 runs with the specified collision, respectively. This record-completeness-selected sample supports a scenario-specific comparison, not a population collision probability or a transferable safety threshold.
Floating Car Data (FCD), traffic information collected by vehicles acting as mobile sensors, are becoming a core input of smart urban traffic networks. This narrative review proposes a five-dimensional taxonomy of FCD technologies (probe vehicle type, data collection mechanism, communication architecture, sampling characteristics, and ownership and access) with multi-source fusion as a transversal layer. It analyses six performance dimensions, reviews six application families and two cross-cutting perspectives (sustainability and equity), and derives seven coupled research directions. Penetration requirements for live monitoring rise by roughly an order of magnitude from coarse highway monitoring (lower bound from one simulation) to high-quality monitoring of urban arterials. Smartphone and taxi positioning supports network-level estimation but not lane-level applications, and no technology class is favourable on all six dimensions. In this purposive sample, 18 of the 90 primary FCD studies use fusion, which outperformed the best single source only on motorways. Although most primary studies are urban, urban field evidence is limited to a few arterials, intersections, and cities, so transfer to dense urban networks requires field validation.
Vehicle arrival headways are among the fundamental microscopic traffic-flow characteristics and provide important information about vehicle interactions within traffic streams. This study investigates the statistical properties of vehicle arrival headways observed under different traffic operating conditions on urban motorway facilities. Traffic data were collected at six locations in the City of Zagreb, including open motorway sections, a tunnel, a bridge, and an underpass. Vehicle speeds and arrival headways were determined from digital video recordings using a frame-by-frame traffic observation methodology. The collected data were analysed using lognormal and shifted-lognormal distributions. The results showed that the observed headway distributions can be adequately represented by shifted-lognormal models. Relationships between the distribution parameters and selected traffic-flow characteristics were then investigated. Particular attention was devoted to average vehicle speed and the share of vehicles travelling in platoons. The analysis revealed the existence of two distinct traffic regimes. The first regime was characterised by higher average vehicle speeds and lower platoon shares, whereas the second regime was characterised by lower average speeds and higher platoon shares. Based on these observations, both a single-regime model and regime-specific models were developed for estimating the parameters of shifted-lognormal headway distributions. An internal assessment procedure demonstrated that the regime-specific models provide better agreement with observed headway distributions within their respective traffic conditions than a single model estimated from the complete dataset. The results indicate that accounting for differences in traffic operating conditions can improve the representation of vehicle arrival headways on urban motorway facilities.
Waste-generation forecasts are useful only if they improve the collection decisions they inform, yet they are usually judged by accuracy alone. We therefore propagate forecasts through an explicit selection-and-routing pipeline in a synthetic benchmark with 30 realizations (three scenarios, 3052 virtual bins, six waste categories, 730 days). Nine forecasters, comprising eight fixed comparator configurations and one separately tuned bidirectional long short-term memory (BiLSTM)–Nyström kernel ridge estimator (Hybrid), are evaluated on common chronological partitions. GRU attains the lowest mean daily root-mean-square error (0.1437 kg/bin/day) and CalendarRidge the lowest advance seven-day error (0.5949 kg/bin); the Hybrid obtains 0.1477 and 0.6878. Daily plastic forecasts are disaggregated to 60 containers with fixed training-period ratios and drive morning stop selection, followed by capacity- and duty-constrained routing. At 120 kg per vehicle, Hybrid-assisted selection reduces unserved overflowing containers from 6.458 to 0.057 per day relative to reactive collection at the cost of 15.4% more travel, and halves overflow relative to a fixed schedule with no distinguishable increase in distance; at 240 kg, it lowers both travel and overflow relative to the schedule. A naive persistence forecast already captures 49% of the overflow reduction attainable with perfect local information, compared with a 53–58% range for the learned forecasters. Saved predictions and route traces permit independent recalculation. The findings are specific to this simulator and do not establish municipal effectiveness.
