Land use and transport shape our cities. The central place of integrated land use transport strategy in improving urban sustainability has been a widespread acceptance. The purpose of this research is to investigate the relationship between land use/land cover types and transport characteristics. Traffic zones, as the spatial analysis unit, are generated by partitioning parcels with single land use/land cover type. Driving accessibility, cycling accessibility and walking accessibility are proposed to quantify the transport characteristics of traffic zones. Taking Eindhoven, the Netherlands as study area, the three accessibility patterns differ but all show strong positive spatial autocorrelation. Clustering method is adopted to synthesize accessibility indicators and group traffic zones with similar transport characteristics into clusters. An contingency table analysis indicates that land use/land cover types are significantly associated with clusters, on which the validation and quantification of experience in the relationship of land use/land cover and transport are based: residential area and commercial area mainly concentrate in high accessible clusters (6 and 7); industrial area and open space tend to favor clusters (4 and 6) with adequate driving accessibility; the high percentage of natural area in medium accessible clusters (4 and 5), especially in less accessible clusters (1, 2 and 3) corroborates the low demands of natural area on mobility service. Clustering map and accessibility patterns can identify the gaps in coverage of mobility service and in efficiency of land use/land cover pattern. The applicability of these tools is demonstrated by two cases. Recommendations for urban planning are obtained from this study considering both land use and transport aspects.

The scarcity of urban land resources requires a well-organized spatial layout of land use to better accommodate human activities, however, as a widely accepted concept, the integration of land use and transport is not given due consideration in land use spatial optimization (LUSO). This paper aims to integrate land use and transport in LUSO to support urban land use planning. Maximizing accessibility fitness, which follows the underlying logic between land use types and transport characteristics, is introduced into multi-objective land use spatial optimization (MOLUSO) modelling to address transport considerations, together with widely-used objectives such as maximizing compactness, compatibility, and suitability. The transport characteristics, in this study, are identified by driving accessibility, cycling accessibility, and walking accessibility. Accessibility maps, which quantify and visualize the spatial variances in accessibility fitness for different land use types, are developed based on the empirical results of the relationship between land use types and transport characteristics for LUSO and addressing policy issues. The 4-objective LUSO model and a corresponding non-dominated sorting genetic algorithm (NSGA-II) based optimization method constitute a prototype decision support system (DSS) for urban land use planning. Decision-makers (e.g., planning departments) can choose an ideal solution to accommodate urban development needs from a set of Pareto-optimal alternatives generated by the DSS. The approaches to creating accessibility maps and MOLUSO modelling are demonstrated by the case study of Eindhoven, the Netherlands. This study advocates limited changes to the current land use pattern in urban planning, and the LUSO emphasizes urban renewal and upgrading rather than new town planning.

The identification of the relationship between land use and transport lays the foundation for integrated land use and transport planning and management. This work aims to investigate how rail transit is linked to land use. The research on the relationship between land use and rail-based transport is dominated by the impacts of rail projects on land use, without an in-depth understanding of the reverse. However, it is important to note that issues of operation management rather than new constructions deserve greater attention for regions with established rail networks. Given that there is a correspondence between land use patterns and spatial distribution of heavy railway transit (HRT) services at such regions, the study area (i.e., the Netherlands) is partitioned by the Voronoi diagram of HRT stations and the causal relationship between land use and HRT services is examined by structural equation modeling (SEM). The case study of Helmond (a Dutch city) shows the potential of the SEM model for discussing the rail station selection problem in a multiple transit station region (MTSR). Furthermore, in this study, the node place model is adapted with the derivatives of the SEM model (i.e., the latent variable scores for rail service levels and land use characteristics), which are assigned as node and place indexes respectively, to analyze and differentiate the integration of land use and HRT services at the regional level. The answer to whether and how land use affects rail transit services from this study strengthens the scientific basis for rail transit operations management. The SEM model and the modified node place model are complementary to be used as analytical and decision-making tools for rail transit-oriented regional development.

Although there is a broad consensus that integrating land use and transport will facilitate sustainable development, land use element is barely considered in network design problem. This paper proposes accessibility to quantitatively describe transport characteristics of a location. With assumptions about the relationship between land use/land cover types and accessibility, this paper introduces a bi-level programming model for land use oriented discrete network design problem (DNDP) and develops a genetic algorithm (GA)-based solution procedure which incorporates Frank-Wolf algorithm for user equilibrium (UE) assignment and Dijkstra algorithm for accessibility measurement. A numerical example is provided to demonstrate the applicability of the model and algorithm. The results indicate that the accessibility of the target traffic zone is improved to meet the demands of commercial areas.

This paper investigates the joint scheduling problem of battery electric bus fleets and plug-in charging infrastructure in an urban transit system. The operation of an electric bus network is inherently a multi-component system, where vehicle assignment, battery energy management, and charger capacity decisions interact and jointly determine system performance and cost efficiency. To capture these interdependencies, we propose a system-level integrated scheduling framework that simultaneously determines bus trip assignments, charging event timing and duration, and charger utilization plans. The problem is formulated as a continuous-time mixed-integer linear programming model that minimizes the total system cost, subject to operational feasibility, battery state-of-charge dynamics, and charger capacity constraints. To enhance computational tractability, a Lagrangian relaxation-based decomposition approach is developed, coupled with a linear programming-based diving heuristic. Computational experiments on benchmark instances demonstrate that the proposed framework produces high-quality system-level schedules with substantially reduced solution time compared with directly using a commercial solver. A real-world case study based on a large charging station in Beijing shows that the optimized joint schedules reduce the required fleet size from 22 to 13 buses and the number of chargers from five to two, leading to a 38.3% reduction in total system cost. These results highlight the effectiveness and practical value of the proposed approach for the planning and operation of urban electric bus transit systems.