Search Results (520)

Rapid urban expansion in arid intermediate cities often produces fragmented urban structures that exacerbate inequalities in public transport accessibility and hinder sustainable mobility. However, the relationships between urban fragmentation, transport accessibility, and travel behavior remain insufficiently explored in pre-Saharan cities. This study investigates these interactions through the case of El Bayadh, Algeria, using an integrated methodology combining urban morphological analysis, Geographic Information Systems (GIS), a household survey of 577 households, traffic counts, institutional interviews, and spatial equality assessment based on the Gini coefficient. Public transport accessibility was evaluated using a 300 m walking threshold, complemented by sensitivity analyses at 400 m and 500 m. The results indicate that approximately 65% of the population lives within 300 m of a bus stop, although coverage is strongly concentrated in central districts. A Gini coefficient of 0.41, provided by the El Bayadh Directorate of Transport and computed across 20 urban zones, indicates moderate-to-high spatial inequalities in accessibility that persist despite increasing the service threshold, suggesting that these disparities are more closely associated with urban fragmentation than with the specific walking-distance threshold used to define service coverage. The transport network comprises 11 bus routes, approximately 160 bus stops, and 90 km of routes; however, limited service quality and uneven spatial distribution contribute to a high dependence on private transport, with 45% of trips made by private car, 35% by taxi, and only 20% by bus. These findings are consistent with accessibility inequalities arising from the mismatch between fragmented urban morphology, centralized urban functions, and the configuration of the public transport network. The proposed framework provides transferable insights for planning more equitable and sustainable mobility systems in rapidly urbanizing arid intermediate cities. Full article

We present a simulation study of bidirectionally pumped distributed Raman amplification (DRA) noise performance for two recirculating-loop span configurations—50 km and 75 km—evaluated over a 50-channel C-band grid (191,200–196,100 GHz, 100 GHz spacing) representative of dense WDM transmission. For each span length, the forward pump power was first optimized on a central channel (193,600 GHz) to minimize signal power variation (SPV), yielding optimum values of 1.2 W for the 50 km span and 2 W for the 75 km span; the backward pump power was then set to achieve 0 dB net gain on the same central channel. These optimized pump conditions were applied uniformly across all 50 channels to characterize on–off gain variation for each single-span configuration. Using an input OSNR of 30 dB, recirculating-loop simulations were then performed for both spans across 1 to 60 (50 km) and 1 to 40 (75 km) round trips, covering a common reach of up to 7500 km. The OSNR degrades more slowly with distance for the 50 km than for the 75 km span up to 7500 km. Under the conditions studied, shorter DRA spans give better noise performance, even though more recirculations are required for the same reach.  Full article

Mobility as a service (MaaS) has emerged as a means to promote multimodal public transport and shared mobility trips, with the “Metro+” integrated mobility approach being its primary form. However, it has recently witnessed major global providers’ bankruptcies. Therefore, this study aims to identify factors driving users’ substitution of ride-hailing by “Metro+” MaaS intermodal trips. Specifically, we took ride-hailing trips as the baseline and constructed a multinomial logit (MNL) model incorporating travel-scenario factors, including trip purpose, weather, and urgency. The results show that travel-scenario factors significantly affected “Metro+” MaaS intermodal trip adoption for urban medium–long-distance trips. Users preferred MaaS intermodal trips for long-distance trips amid clear weather and no time pressure, and they favored ride-hailing in extreme weather conditions or for time-sensitive trips. Therefore, MaaS providers should emphasize travel scenarios in their marketing messaging. Finally, marginal rate of substitution (MRS) and elasticity analyses were conducted, and several strategies were proposed: (1) increasing ride-hailing availability; (2) improving transfer facilities and conditions; and (3) implementing dynamic demand matching. The study facilitates the identification of market opportunities for MaaS instead of car usage, contributes to MaaS marketing and service strategy optimization, and promotes sustainable urban transportation system development. Full article

Air travel induces substantial landside traffic through the movement of passengers, employees, suppliers, and cargo between airports and their surrounding regions. While this airport-induced landside traffic has received growing attention within airport sustainability research, its associated external costs remain insufficiently quantified. This study develops a modular model to estimate traffic volumes and associated external costs of airport-induced landside traffic. It accounts for key behavioral and operational parameters, including modal split, trip distances, occupancy rates, and trip frequencies, differentiated across user groups and transport modes. The model is applied to Germany as a case study using national mobility statistics, airport data, and external cost factors from European transport studies. The assessment covers greenhouse gas emissions, air pollution, accidents, noise, habitat damage, and upstream fuel supply impacts. Results indicate that airport-induced landside traffic generated external costs of approximately EUR 1.43 billion in Germany in 2019, with passengers and airport employees accounting for the largest shares. Accident costs and greenhouse gas emissions dominate the overall impacts. Sensitivity analyses further show that moderate behavioral changes, such as modal shifts toward public transport and increased vehicle occupancy, can significantly reduce external costs. The findings highlight the importance of integrating landside access into environmental assessments and sustainable airport planning. Full article

In high-density urban areas, pedestrian route choice behavior is influenced differently by the attributes of the objective environment, depending on the purpose of the trip. To explore this issue, this paper first constructs the traditional Path Size Logit (PSL) model to perform baseline estimation of the effects of objective attributes such as path distance, intersections, number of lanes, greenery, and commercial facilities. It then introduces a hierarchical Bayesian framework to build the Hierarchical Bayesian Path Size Logit (HB-PSL) model, using the No-U-Turn Sampler (NUTS) for posterior sampling to quantify parameter uncertainty and capture inter-group heterogeneity. The model achieves inter-group information sharing through a hierarchical prior structure and uses Automatic Differentiation Variational Inference (ADVI) to provide rapid approximate estimation. An empirical analysis shows that different importance is given to objective environmental attributes for different travel purposes. Furthermore, this paper proposes the “Equivalent Distance (ED)” index, which transforms the preference of different groups for different environmental attributes into an actionable spatial length. For shoppers, the greening level increases by one level for every 100 m, which is equivalent to shortening the path by about 31.31 m; commercial facilities increase by one for every 100 m, which is equivalent to shortening the path by about 76.63 m. The results provide behavioral support for the formulation of differentiated walking strategies in high-density urban areas: priority should be given to strengthening the green coverage and commercial facility layout in commercial blocks to synergistically improve walking efficiency, safety, and comfort. Full article

This study presents an age-based gravity-model framework that applies the conventional gravity model under different age-group population scenarios to examine trip distribution patterns (spatial interaction patterns) in a high-density urban environment. Gaza Governorate was selected as the study area due to its high population density, constrained spatial structure, and intensive daily mobility demands. Data were collected in 2023 through a household survey involving 600 households, direct observation of passenger movements, and travel impedance data consisting of travel distance and travel time between study areas. Age-group populations were classified into three categories (0–14 years, 15–64 years, and 65 years and above) and incorporated as demographic mass variables within separate gravity-model scenarios. Model performance was evaluated using Pearson correlation (R), coefficient of determination (R²), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The validation results demonstrated acceptable to strong agreement between the survey-derived and observed OD matrices across all age groups. Following calibration, the gravity model successfully reproduced the general spatial concentration of observed trip distribution patterns between origin–destination (OD) zones within the study area. The conventional model and the 15–64 and 0–14 age-group scenarios exhibited moderate explanatory capability. Among the evaluated scenarios, the 15–64 age group achieved the highest coefficient of determination (R² = 0.518), followed by the 0–14 group (R² = 0.485), while the 65+ group achieved the lowest value (R² = 0.339). The results indicate that age composition is associated with variations in the strength and predictability of aggregated transportation interaction patterns, although the overall spatial interaction structure remained broadly consistent across all age groups. The findings suggest that applying the conventional gravity model under age-group population scenarios provides a useful exploratory framework for analysing transportation interactions in data-constrained, high-density urban environments. The proposed framework contributes to the growing literature on age-related mobility by demonstrating how age-group population structures can be incorporated into a conventional OD-based gravity-model framework for comparative trip distribution analysis and may support evidence-based and sustainable trip distribution modelling for transportation planning in data-constrained urban environments. The proposed framework provides an interpretable and data-efficient approach for supporting sustainable urban transportation planning by improving the understanding of age-specific trip distribution patterns in data-constrained high-density urban environments. Full article

Driver behavior is a critical factor in road safety, contributing to the majority of traffic crashes. The i-DREAMS project introduced the concept of a Safety Tolerance Zone (STZ) to enhance driving safety through real-time and post-trip interventions. This study develops and evaluates three hybrid machine learning models—(i) Deep Neural Network–Random Forest (DNN-RF), (ii) Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM), and (iii) Recurrent Neural Network–AdaBoost (RNN-AdaBoost)—to classify risky driving behavior into three safety levels using naturalistic driving data from Belgium and the UK. The dataset includes 69 drivers, 15,389 trips, and 265,512 min of driving data. Among the models tested, the DNN-RF model demonstrated the highest accuracy, reaching 98% in Belgium and 97% in the United Kingdom, outperforming other approaches. Feature importance analysis identified harsh acceleration and braking as the most critical factors in Belgium, while total trip distance and harsh acceleration were predominant in the UK. To enhance model transparency, we applied the Local Interpretable Model-agnostic Explanations (LIME) algorithm, providing valuable insights into model predictions. The findings support the potential of hybrid deep learning models in improving road safety by accurately detecting risky driving behaviors. These insights can inform targeted interventions and driver assistance technologies to mitigate crash risks and promote safer driving practices. Full article

Proximity-based planning provides a normative framework for organising everyday services, but local accessibility does not necessarily correspond to the spatial extent of observed destination use. Building on the distinction between normative proximity and grid-level aggregated revealed activity space, this study develops an LBS-based diagnostic framework for planned–used life-circle mismatch. Anonymised non-home, non-work home-grid–destination-grid links observed at least four times in aggregate during the month were analysed for 995 residential grids of 500 m × 500 m in Lanzhou, China. The framework evaluates five dimensions: boundary coverage, activity-space scale, centroid displacement, directional morphology, and destination environments, and translates them into four planning-diagnostic types. The results reveal widespread divergence between planned proximity and revealed use. On average, 82.0% of aggregated recurrent visit weight lies outside the corresponding 15 min walking boundary, while the median distance between residential and activity centroids is approximately 4.18 km. The outside-dominant pattern remains evident when the analysis is restricted to destinations located in selected basic-service-dominant POI environments. Greater local POI supply is generally associated with lower outside-boundary reliance, whereas bridge distance is more clearly associated with centroid displacement than with outside-boundary share. The explanatory models have modest fit and identify grid-level associations rather than causal behavioural mechanisms. These findings support treating the 15 min boundary as a normative benchmark for local service provision rather than a presumed container of residents’ daily activities. Planning evaluation should therefore combine accessibility standards with behavioural evidence and differentiated diagnosis. The LBS data do not identify trip purpose, travel mode, socioeconomic characteristics, service quality, or individual necessity, and the results should be interpreted as grid-level diagnostic associations. Full article

Environmental protection has become a key priority for the Chinese government. This study examined the symmetric complementarity of spatial layouts between inter-city high-speed rail and urban subway systems. Their co-location formed a structurally symmetric low-carbon travel chain that bridged long-distance and short-distance trips. The study evaluated their joint effect on urban carbon productivity within a difference-in-differences framework. The baseline two-way fixed-effects DID estimate showed that the simultaneous operation of HSR and subway systems significantly improved urban carbon productivity, with an average increase of approximately 10.51% in treated cities relative to non-treated ones. This core finding remained qualitatively robust across a series of tests, including conditional coarsened exact matching (CEM), placebo simulations, sample exclusions, and decomposition of joint effects relative to individual rail impacts. Complementary nonparametric causal forest estimates yielded smaller but still significant effect sizes, providing supportive evidence for causal validity under weaker functional form assumptions. Heterogeneity analysis based on dynamic GDP grouping revealed that high-GDP cities experienced stronger emission reductions, while low-GDP cities benefited more from the economic growth dimension of carbon productivity. Empirical patterns aligned with the theoretical prediction of the transportation substitution mechanism. The positive effect of dual rail availability on carbon productivity was stronger in cities with higher private car stock, as shown by double machine learning interaction models. This finding only provided indirect evidence and did not constitute direct proof of actual modal shift behavior. The results exhibited magnitude asymmetry between parametric and nonparametric methods, yet all estimates consistently pointed to a positive qualitative direction. This consistency revealed directional symmetry in the causal conclusion. The study contributed by (1) examining the combined carbon productivity effect of co-located HSR and subway systems, (2) identifying the moderating role of private car stock in the transportation substitution mechanism, (3) revealing development-stage-based heterogeneity, (4) and combining machine learning methods with parametric causal inference as complementary robustness evidence. Policy implications suggested that high-GDP cities should prioritize expanding dual rail coverage and optimizing connections to amplify emission reductions, while low-GDP cities should focus on improving HSR connectivity with existing public transit systems and fostering low-carbon industrial development in line with local conditions. Full article

Reverse engineering of printed circuit boards is still largely manual when original computer-aided design documentation is unavailable. This paper presents a semi-automatic vision-based pipeline that prepares an editable KiCad schematic draft for use in an Electronic Design Automation (EDA) workflow from paired TOP and BOTTOM board images. The method combines color-profile estimation, pad and through-hole detection, trace segmentation, optical character recognition, component inference, an explicit evidence graph and schematic export with drawn wires. A separate readability step aligns symbols to a grid and reroutes the reconstructed nets with orthogonal wires; it does not change the reconstructed netlist. The primary quantitative evaluation used twelve synthetic KiCad fixtures and three solver configurations: the default sequential pipeline, an opt-in global component solver and an opt-in probabilistic contact solver. These fixtures provide controlled regression cases and are complemented by a small exploratory acquisition trial on real photographed boards. All configurations completed all runs and passed the export round-trip validation without falling back to label-only connectivity. This round-trip check confirms consistency between the internal reconstruction and the exported schematic, but it is reported separately from electrical correctness against the KiCad reference design. The stricter reconstruction-quality criterion still failed on four stress cases involving repeated component chains, long meandering variable-width traces, circular distractors near pads and two-sided transistor layouts. The probabilistic contact solver was therefore kept as an opt-in diagnostic mode rather than enabled by default; it reduced the global pin-to-pin netlist edit distance from 642 to 525 while preserving schematic export checks. The real-board trial indicates that pad and hole detection can transfer to simple photographs, with trace extraction remaining sensitive to uncontrolled illumination and weak copper contrast. The results support the use of the system as a human-in-the-loop reconstruction assistant and identify component grouping, trace-contact reasoning, real-photograph benchmarking and safe missing-edge activation as the main remaining research problems. Full article

Grocery shopping remains a heavily car-dependent activity in urban areas, even for short-distance trips within residential neighbourhoods. A primary barrier to shifting toward active travel (walking or cycling) is the physical burden of carrying heavy or bulky goods. This study investigates whether a retailer-managed home delivery service could encourage consumers who currently rely on motorised modes for grocery shopping to shift towards active travel while preserving the in-store shopping experience. The analysis focuses on urban Italian consumers who currently use motorised modes for grocery shopping. Using a Stated Preference (SP) experiment and a Mixed Logit (MMNL) model (n = 88), we analyse the conditions under which such a service may encourage the adoption of active travel modes and support proximity-based shopping patterns. Given the exploratory nature of the study and the small, non-representative sample, the findings should be interpreted as preliminary evidence for urban motorised grocery shoppers rather than as representative of the Italian population. The results indicate a substantial willingness among respondents to adopt the proposed service configuration. Delivery time, service cost, and the availability of delivery time-window selection emerge as critical factors influencing consumers’ choices. Acceptance of the service is also influenced by perceptions of walking and cycling infrastructure quality, trust in the integrity of delivered groceries, preferences for local products, and concerns regarding the working conditions of delivery personnel. Additionally, the model reveals significant heterogeneity in preferences regarding delivery by drone/autonomous vehicle and a 100% reduction in greenhouse gas emissions relative to conventional motorised transport. Younger respondents exhibit a more favourable attitude towards automated delivery technologies, while differences in the valuation of environmental benefits emerge between male and female respondents. The findings suggest that retailer-managed home delivery may represent a promising mechanism for encouraging active travel among current motorised grocery shoppers, while maintaining consumers’ relationship with neighbourhood retail services. These results provide retailers and urban policymakers with valuable insights, suggesting that appropriately designed delivery services may support more sustainable and proximity-oriented shopping behaviours. Such services could potentially contribute to maintaining the accessibility and vitality of neighbourhood retail activities, particularly in ageing urban contexts. Full article

The electrification of long-haul freight transport requires a comprehensive public charging infrastructure along motorways. This study presents a framework combining multi-agent transport simulation (MATSim) with evolutionary bi-objective optimization (NSGA-II) to determine the number and spatial distribution of high-power charging (HPC) points for battery-electric trucks (BETs) on the German motorway network. Beyond infrastructure sizing, the approach also quantifies the impact of BET charging on the duration and distance of long-haul truck trips. The optimization simultaneously addresses the perspectives of two key stakeholders: charge point operators (CPOs), who seek to maximize charger utilization, and logistics operators, who aim to minimize waiting times. The results yield a range of Pareto-optimal configurations balancing the two objectives. A multi-iteration replanning step further lets trucks adapt their routes to experienced waiting times for a more realistic performance assessment, reducing mean waiting times by up to 92%. We evaluate five electrification levels from 1% to 20% across two charging network scenarios with 347 and 779 potential locations, respectively. For the balanced solutions—the knee-point configurations that best reconcile both objectives—at a 10% electrification level, the optimized network reaches a temporal charger utilization of 23% to 32% at mean waiting times of about 1.4 to 1.9 min per charging process. Compared with an internal combustion engine truck (ICET) reference, BET trip durations increase by only 0.9% to 1.3% due to charging detours. Overall, the fast-charging network planned by the German federal government appears sufficient for the HPC demand at electrification levels up to 10% to 15%, whereas additional low-power charging (LPC) infrastructure beyond the planned locations will be needed to cover overnight charging requirements. Full article

Understanding the advance ticket purchase behavior of air–rail intermodal passengers is essential for travel demand forecasting, schedule coordination optimization, and revenue management. Using actual booking data, this study investigates passengers’ advance purchase time (APT) decisions. A Bayesian network (BN) model integrating expert knowledge and data-driven learning is established, with socioeconomic attributes and ticket characteristics as input variables and APT as the output variable. Based on this BN, group analysis is conducted across four routes of varying distances: Tianjin–Shanghai, Tianjin–Changsha, Tianjin–Guangzhou, and Tianjin–Sanya. The results indicate that socioeconomic and ticket attributes influence advance purchase behavior both directly and indirectly through interactive effects. Inferential analysis reveals that elderly passengers, male travelers, morning departures, and longer-distance trips are associated with earlier ticket purchases. Sensitivity analysis shows that fare, transfer time, and age exert heterogeneous effects across routes. Compared with short- and medium-haul journeys, airfare, rail travel time, and transfer time impose stronger impacts on long-haul intermodal trips. Finally, targeted revenue management strategies are proposed to improve air–rail ticket sales. Full article

To scientifically explore the vehicle capacity characteristics of circular earthwork transportation routes in water conservancy projects, this paper takes the second-phase project of the Huaihe River Sea Entrance Channel as the research background. Key influencing factors such as road conditions, vehicle performance parameters, safe car-following distance, and earthwork loading–unloading duration are comprehensively considered, and a cellular automaton simulation model is constructed. Horizontal comparative verification is carried out with the Intelligent Driver Model, System Dynamics model, and field measured data to verify model accuracy. The results reveal that the cellular automaton (CA) model yields a total vehicle transport trip count of 606, with a MAPE of 0.66% when compared against the field-measured average of 602 trips. The simulated average travel speed reaches 16.71 km/h, corresponding to a MAPE of 2.89% relative to the field measurement of 16.24 km/h. The error metrics of these two indicators are markedly lower than those derived from alternative models. Due to differences in modeling paradigms and applicable mechanisms, the three models exhibit distinct characteristics in simulation performance. Among them, the cellular automaton model is more suitable for the circular earthwork transportation scenario of this study, which can accurately reflect the coupling characteristics of microscopic traffic behaviors such as multi-route confluence and node queuing, and has high consistency with actual engineering operation. Sensitivity analysis indicates that improving earth loading efficiency and reasonably arranging excavator quantity can significantly enhance the overall transportation efficiency. The modeling ideas and simulation analysis method adopted in this paper are not only applicable to the specific engineering scenario, but also can be extended to similar water conservancy earthwork transportation and large-scale engineering logistics transportation fields. It can provide theoretical basis and engineering reference for earthwork scheduling optimization and quantitative calculation of traffic capacity in water conservancy projects. Full article

The integrated usage of metro and bus services plays a key role in long-distance trips in big cities. Revealing the nonlinear relationship between the intermodal transfer demand and the built environment is significant for building a sustainable public transport system. This paper proposes a stacking ensemble explainable machine learning framework, which uses meta-learner to learn the prediction results of diverse base learners to improve performance, to detect how the impact factors impact the intermodal demand, including metro-to-bus and bus-to-metro directions. In this framework, the ensemble model is the stacking model; the ridge regression model is the second model. The base learners contain tree-based models (e.g., Random Forest, XGBoost and CatBoost) and non-tree-based models (e.g., SVR and KNN). The framework is applied to the case study of Beijing, China, based on one weekday (13 May 2019) and one weekend day (18 May 2019) of smart card data covering the main urban districts within the Sixth Ring Road. The results indicate that the stacking ensemble learning model outperforms the base learning models. For the metro-to-bus direction, transfer time, bus station count, and degree centrality are the top three influential factors; for the bus-to-metro direction, transfer time, bus station count, and shopping POI count are the top three, with lower predictive performance due to greater variability in this direction. However, the interaction effect of transfer time and bus station count is negative. This study could provide new insights into public transport planning and management. Full article