The high level of connectivity between cities is leading to increased intercity travel with intercity passenger service requiring close collaboration station access and egress modes. Thus, there is a need to account for intermodal travel behaviour not only within the city, but also between cities. In this paper, intermodal travel behaviour is defined as a three-stage combined mode choice based on a review of related concepts in the literature. Representative intermodal travel behaviour models are reviewed with typical applications of these models. In addition, the gaps in existing studies are summarised with directions for future research.

Public infrastructure construction must be carefully coordinated with the construction of multi-center, multi-level, multi-node urban networks, especially transportation networks, to balance regional traffic demands. The hierarchical facility location problem (HFLP) optimizes facility locations so that the network provides efficient and economic services, which is a network design problem with various resources and limited services. This problem has attracted much attention because of the increased complexity of facility services and the diversified demands. This review of HFLP research over the past forty years, especially the past six years, shows that the main research trends involve traditional fields such as healthcare, transportation hubs, and supply chains. In order to balance multiple system planning requirements with the various characteristics and scales of various types of network facilities, and to solve problems related to the mismatch between the availability of system services and the actual needs, multi-objective nested models, dynamic and random constraints, the efficient optimization of heuristic algorithms with important issues like various search and stopping criteria have become the main research trends. Studies have also analyzed the supply chain reverse logistics system for product inspection and recycling, the design of waste storage facilities using the echelon classification processing model, and the optimization of transportation hub locations that serve passenger needs. With the improved service levels and efficiency requirements, the hierarchical concept can improve services for a broad range of applications in various areas such as the hierarchical location of urban agglomeration service facilities and the layouts of centralized electrical vehicle charging stations.

Residents are demanding better conditions for urban non-motorized traffic. However, the classical forecasting model for non-motorized traffic has mainly focused on large city areas and existing roads, which cannot fully meet planning demands for the construction or extension of urban bicycle paths. This study used an improved multi-scale geographically weighted regression (MGWR) model to predict the flow of non-motorized traffic. The MGWR model includes the influence of land development and utilization, road network structure, and the nearby public transport on the bicycle flows. The area along the Beijing to Zhangjiakou Railway was used as an example for a case study using bike sharing data in this region. The results show that the MGWR model has better predictive ability with the geographically weighted regression model R² increased by 0.087 and the multiple linear regression with R² increased by 0.113. Finally, this paper gives an approach for non-motorized traffic planning and management based on key factors affecting non-motorized traffic with appropriate ranges of the independent variables.

This study analyzed the influence of psychological latent variables, urban agglomeration, emergencies and other factors on intercity travel mode choices in large cities. Data from the Beijing-Tianjin-Hebei urban region was used to estimate an integrated choice and latent variable (ICLV) model for the empirical analysis. The results show that the goodness-of-fit of the ICLV model, which included convenience and ride experience, is better than that of the traditional discrete choice model. Origin and destination, travel distance and frequency of train services all significantly impact the choice of travel mode. The probability of train use in hazy weather increased by 25.69%, the probability of car use in rainy weather increased by 31.63%, the probability of car use in snowy weather decreased, and the probability of car use when the trains were blocked increased. These results are helpful for understanding intercity travel mode choices and provide a scientific basis for predicting travel volume and demand control for intercity travel during emergencies.

China's high-speed railway network is becoming more developed and urban agglomerations are gradually expanding with significantly more new urban agglomerations. Passenger transportation modes need to be further studied to meet the needs of passengers traveling across urban agglomerations on high-speed rail networks. This study used an improved multinational logit (MNL) model to calculate the passenger flow loss rate of various transportation modes and constructed a model for optimizing high-speed railway network passenger transport based on the need for cross-urban group travel. The core nodes in the network were identified using a network node measurement algorithm based on the entropy weight technique for order preference by similarity to ideal solution (TOPSIS) algorithm. Transition nodes in the passenger flow were identified using a transition multiplication selection algorithm based on network centrality. The model was then applied to the network composed of 22 cities with node measurement values above 0.15 to analyze full and partial network connections. The results show that the total passenger flow rate on the high-speed railway networks for cross-city travel can be increased by 11.54% with 90 fewer trains.

A bi-level optimization model was developed for planning charging facility deployment on an intercity highway network. The model balances the variable charging demand and the construction time sequence. The lower model is based on a dynamic traffic assignment model. The spatial-temporal distribution of the charging demand at equilibrium was obtained from simulations of multi-user driving and charging behavior. The upper model optimizes the charging station's locations and capacities using construction time sequence and level of service (LOS) constraints to minimize the operator investment. The model is then used to analyze the intercity highway network between Jinan and Qingdao in the Shandong Peninsula urban agglomeration. The model provides reliable estimates of the real-time charging system LOS and a satisfactory layout based on the user chargig preferences, network traffic conditions, and facility conditions. The layout was then analysed using forward and reverse construction time sequences. The results show that for the same LOS constraint, long-term deployment should consider future energy demands and the planners should use the reverse construction time sequence method.

The discomfort, inconvenience, and delays caused by congestion, environmental exposure and long wait time for conventional buses, shared bicycles or taxis for short feeder trips to and from subway stations are often the main reasons for passengers to quit public transportation. The wide variety of passenger service quality requirements for punctuality, speed, and comfort are difficult to fully satisfy when assuming demand homogenization. This study presents a feeder transit routing optimization method for driverless electric buses for meeting the heterogeneous demands of travelers. In the demand generation stage, users choose personalized indicators such as punctuality, speed, and comfort so that the model can fully consider the impact of personal preferences on the objective function. A vehicle route planning model is then developed with multiple routes and multiple soft time windows with the Tabu search algorithm used to solve the problem. A questionnaire is used to survey the user preferences for each demand index for a case study of the Beijing Fengtai Science and Technology Park subway station and its surrounding area of 4 km². This method significantly improves the ability of the planning results to satisfy heterogeneous commuter demands than the traditional method.

Urban travel demands are rapidly growing in metropolitan areas as urbanization spreads in China. This paper studies how contributing factors such as income level, traffic congestion affect residents choices of work locations, residence locations and commutes in metropolitan areas. A social optimal model was developed with these choices to capture the trade-off between the agglomeration and congestion in a two-city system consisting of a big city and a small city. The boundary curve of the spatial distribution of inter-city commuters was obtained by analyzing the commuting cost in consideration of traffic congestion, and calculated the net income of the system which is contributed by both intra- and inter-city commuters. Thus, the model needs to show the effects of resident location, work location and commuting on the system which depend on income levels, commute costs, and housing costs.This urban optimization problem was designed as a nonlinear programming problem that gave the optimal spatial distribution of residential and work locations and the number of commuters between cities.An analysis of the Shanghai-Jiaxing two city system shows that the current Shanghai population is higher than the optimal population.The social welfare can be enhanced by encouraging more people working in Shanghai to live in surrounding small cities with intercity railway commutes.

Urban clusters are important in China's development. The integration of transportation systems in these urban clusters is one of the most important tasks for promoting the development of these clusters, which requires a full understanding of the current travel patterns. Location-based service (LBS) big data for the Guangdong-Hong Kong-Macao Greater Bay Area was analyzed to identify the commuter travel scaling patterns. The results show that most commuting trips are still inside the cities, but there are sizable inter-city commutes in some areas. The results also show that the probability density of the commute distance is exponentially related to the distance. In addition, the scaling parameter for intra-city commuting is generally larger than that of inter-city commuting, which indicates that trips within cities are more sensitive to the commuting distance. The scaling parameter further reflects the connections between the transportation and land use, which can be useful for policy making.

The scale and spatial distribution of travel demand are important for transportation planning. The stable travel demand network among 306 counties in the Yangtze River Delta (YRD) was extracted from mobile phone signaling data. The scale and spatial distribution characteristics of intercity travel in the YRD were analyzed from nodes and edges based on the complex network analysis method. The results show that extensive intercity travel links have been formed in the YRD. However, only a few counties within Nanjing, Suzhou, Wuxi, Changzhou, Shanghai, Hangzhou, Zhenjiang, Shaoxing, and Chizhou etc. have high-intensity intercity travel. Moreover, the economic scale, geographical location, and transportation conditions of counties affect the capability and intensity of intercity travel. The spatial distribution of intercity trips has evident spatial proximity and intensity hierarchy. High-intensity trips are responsible for 39.4% of the trip intensity with 0.05% of the number of links. The intercity trips still have a centripetal nature.

The ‘big city diseases’ is caused by population expansion in metropolises. This paper shows how the government can take tax regulation to balance the goal of decreasing the population against that of maximizing the social welfare. Considering the effects of commuting tax and payroll tax on population distribution, spatial structures, and land rents in the urban agglomeration, a three-city spatial equilibrium model that allows for intra-city commuting, inter-city migration, and inter-city commuting, is presented. The numerical results of three classical urban agglomeration spatial structures show that the effects of tax policies are related to the urban agglomeration spatial structures, but a well-designed commuting tax is always a better choice for the government.

Road-rail intermodal travel is one of the important intercity travel modes. However, an intercity travel recommendation method based on single factor ranking cannot satisfy the personalized travel demands of road-rail intermodal passengers. This study improves travel efficiency by using a profile database based on passenger historical ticketing data with the term frequency-inverse document frequency (TP-IDF) and K-means algorithms to explore the road-rail intermodal travel demand differences derived from the passenger heterogeneity. The model uses reward functions based on preference scores and sensitivity characteristics with the Q-learning reinforcement learning algorithm in a road-rail intermodal travel recommendation method based on the passenger heterogeneity profile. The method is applied to the Tianjin-Sihong route as a typical road-rail intermodal travel route from a megacity to small cities with road-rail intermodal travel schemes recommended for three types of passengers with different sensitivities. The results show that the recommended travel schemes shorten travel times by 20% and reduce travel costs by 32% while effectively meeting passenger behavior preferences, sensitivity characteristics and personal demands.

The resilience of multimodal transportation networks in urban agglomerations to attacks was analyzed using a network structural resilience assessment model based on complex network theory and resilient city theory which considered the absorbing capacity, buffering ability and recovery ability of the networks. The network resilience was calculated by a space vector modulus. Data for road and railway passenger transport between cities in the Guanzhong Plain urban agglomeration (GZP agglomeration) was used to construct the regional transportation network. The topological characteristics of the multimodal transportation network were then analyzed using the ArcGis and Ucinet tools. Each node's geographical location and transport connections with the surrounding area were used to identify key nodes based on the node importance. Analyses of the effects of attacks on these key nodes in the multimodal transportation network showed that the network had poor buffering ability, especially after failure of the Caijiapo station when the buffer value decreased to 0.388 9. The system had very weak ability to return to the normal state after a site with a large node degree failed. Failures of the general railway station and the high-speed rail station had far greater impacts on the structural resilience than failure of the highway terminals. Analysis of the characteristics of the key nodes led to suggestions for improving the network structure resilience.

Green transportation systems are an important part of environmentally balanced societies. A green transportation level measurement model is needed to enable analyses of the spatial-temporal evolution characteristics of green transportation systems for green transportation development policies. This study used the driver-pressure-state-impact-response (DPSIR) model to analyze the interactions between various sub-systems of green transportation systems in complex urban agglomeration environments, along with the economics, resources and environmental impact. The green transportation level measurement index for urban agglomeration systems used index weights weighted by a combination of the intuitionistic fuzzy analytic hierarchy process (IFAHP) and entropy weighting. Then, a set pair analysis with variable fuzzy set model was utilized to construct a comprehensive measurement model for urban agglomeration green transportation levels. The contribution degree and obstacle degree models were used to identify the key factors affecting and restricting urban agglomeration green transportation transportation levels. Finally, the models are applied to the Guanzhong urban agglomeration(GUA). The results show that this green transportation system is a general level system and its temporal evolution trends have "N" stage characteristics. The system spatial evolution is "high in the west and low in the east, and high in the south and low in the north" with the gaps between cities gradually narrowing. The model results are more accurate than those of the traditional model and better reflect the fuzziness of urban agglomeration transportation systems. In the criterion level, the state regulations have the greatest effect on the system development with a significant role in promoting the green transportation development in this area while the driving factors have the lowest effect. In the indicator layer, the key factors restricting the green transportation development are the average daily bus passenger volume, the road streetlight coverage, the average number of buses per 10 000 people, the per capita GDP and the per capita park green space area. The research conclusions provide theoretical support for promoting the development of green transportation systems in urban agglomeration, low-carbon transformation of the transportation industry, and the goal of a carbon neutral society.