Residential Travel Research Articles

Who will do more? The pattern of daily out-of-home activity participation in elderly co-residence households in urban China

Over the last several decades an on-going interest by academics in studies of intra- and inter-personal relationships has revealed complex and dynamic decision-making mechanisms within households. However, previous research has mainly focused on the relationship between partners/spouses in households of couples and/or nuclear families. The intra-household dynamics in household contexts other than couples/spouses and inter-personal relations in these other households remain largely unknown. This paper is therefore dedicated to investigating the intra- and inter-personal interactions of partners/spouses and inter-personal interactions between different generations in elderly co-residence households. Using data from the Nanjing Residents Travel Survey 2012, an asymmetric structure is observed among the interactions between young females and males: males' activity participation is more strongly affected by females' activity engagement than the reverse. The presence of an elderly mother or mother-in-law tends to reduce the interdependence of intra- and inter-personal interactions for both young couples. It is interesting to find that a young female's activities are more determined by her elderly mother in the extended family than her husband. The influence of a young spouse on an old mother's activity participation is also gender-specific. Implications of these findings for public policies are also discussed at the end of the paper.

The Effect of Traffic Facilities Accessibility on Residents’ Travel under Block Scale

The development of urbanization and motorization has changed the way of life of residents. It greatly increases residents’ dependence on motor vehicles, thus causing a series of traffic travel problems. The development of road traffic has become an important research perspective. Especially, the influence of traffic facilities accessibility on residents’ travel is of great significance. Based on the questionnaire and SPSS analysis software, this paper discusses the influence of traffic facilities accessibility on the travel mode and distance of residents under the influence of their individual social attributes. The results show that whether the residents own cars, their occupation and their monthly income have influence on the travel distance. Under the influence of control, the number of bus lines, the number of intersections, the distance from the nearest bus station and the distance from the residents travel are negatively correlated, that is, the more bus lines, the more intersections, the farther away from the nearest bus station, the shorter the travel distance of residents. And the way of residents travel is basically determined by the factors of residents’ social attributes and residents’ travel distance. Traffic construction needs to fully consider the needs of different residents.

Research on travel network structure based on normalised Laplacian spectrum

To study the residents' trip-activity chain and patterns, we start from the residents' trip-activity network. The normalised Laplacian spectrum from the local metric features described the topological structure information of network with the statistical analysis method of complex network. The statistical analysis of complex networks failed to show the global information of the network and described the network structure more completely, intuitively and conveniently. The normalised Laplacian spectrum is used to portray the subway network, the airline network and the macro-laws of the network from residents travel. In this paper, we analysis other networks and classify networks qualitatively. The results proved that the normalised Laplacian spectra are an efficient tool for analysing macro-structural or micro-structural features of geographic networks.

Research on travel network structure based on normalised Laplacian spectrum

To study the residents' trip-activity chain and patterns, we start from the residents' trip-activity network. The normalised Laplacian spectrum from the local metric features described the topological structure information of network with the statistical analysis method of complex network. The statistical analysis of complex networks failed to show the global information of the network and described the network structure more completely, intuitively and conveniently. The normalised Laplacian spectrum is used to portray the subway network, the airline network and the macro-laws of the network from residents travel. In this paper, we analysis other networks and classify networks qualitatively. The results proved that the normalised Laplacian spectra are an efficient tool for analysing macro-structural or micro-structural features of geographic networks.

Evolution trend analysis of urban residents’ low-carbon travel development based on multidimensional game theory

In the travel process of urban residents, travelers will take a series of activities such as imitation and exclusion by observing other people’s travel modes, which affects their following trips. This process can be seen as a repeated game between members of the travelers. Based on the analysis of this game and its evolution trend, a multi-dimensional game model of low-carbon travel for residents is established. The two dimensional game strategies include whether to accept the low-carbon concept and whether to choose low-carbon travel. Combined with evolutionary game theory, the low-carbon travel choices of residents in different cities are simulated, and the evolutionary stability strategies are obtained. Finally, the influences of the main parameters of the model on the evolution process and stability strategies are discussed. The results show that travelers would develop towards two trends. Cities with more developed public traffic system have a higher proportion of receiving low-carbon concept and choosing low-carbon travel. Cities with underdeveloped public transport system could increase this proportion by some measures such as encouraging residents to choose slow transport and increasing the propaganda of low-carbon travel, but the positive effects of the measures like propaganda have a limited impact on the proportion.

Exploring impacts of the built environment on transit travel: Distance, time and mode choice, for urban villages in Shenzhen, China

Context-specific research are necessary to promote public transit by optimizing the built environment in the process of urban renewal. Using data of residential travel survey in Shenzhen, China in 2014, this paper investigated how built environment affected transit travel in urban villages where low-income group resided, and they are all tenants. Structural equations modelling was employed to express the effects of socio-economic, public transit service related and built environment variables on travel mode choice, travel time and distance, with control for residential self-selection. It is found that after considering the self-selection effect, the influence of density and transit availability on travel distance, travel time and transit mode choice in urban villages are still significant. For urban villages, transit availability matters greatest for transit promotion, but the negative effect of density on transit mode choice and none effect of mixed land use are unusual as previous findings. This is partly because of relatively low income, low car ownership, and less elastic travel demand of tenants living in urban villages. Our findings provide some insights into transit-oriented urban renewal that when transforming urban villages, emphasis should be put on enhancement of transit availability, and the mixed land use could be put in the last consideration when there is limited time and funds.

Spatial-temporal Distribution Prediction of Charging Load for Electric Vehicle based on Dynamic Traffic Flow

On the basis of velocity-flow-density relationship and traffic-energy consumption relationship, this paper proposes a prediction method of the spatial and temporal characteristic of electric vehicle charging load using the traffic data. By analyzing the residential travel data, a probability model was built to generate trip chains of a day, which contain destination and start time. Then vehicle transfer model was used to simulate driving vehicles on the roads and SOC could be calculated by the road condition and temperature. Drivers would charge vehicles when SOC is below the charging threshold. Finally, by using the Monte Carlo method, charging load of a real traffic model was calculated according to charging demand from all electric vehicles at different time and location during the area.

Identifying trip ends from raw GPS data with a hybrid spatio-temporal clustering algorithm and random forest model: a case study in Shanghai

ABSTRACTSmartphones have been advocated as the preferred devices for travel behavior studies over conventional surveys. But the primary challenges are candidate stops extraction from GPS data and trip ends distinction from noise. This paper develops a Resident Travel Survey System (RTSS) for GPS data collection and travel diary verification, and then uses a two-step method to identify trip ends. In the first step, a density-based spatio-temporal clustering algorithm is proposed to extract candidate stops from trajectories. In the second step, a random forest model is applied to distinguish trip ends from mode transfer points. Results show that the clustering algorithm achieves a precision of 96.2%, a recall of 99.6%, mean absolute error of time within 3 min, and average offset distance within 30 meters. The comprehensive accuracy of trip ends identification is 99.2%. The two-step method performs well in trip ends identification and promotes the efficiency of travel survey systems.

Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data

Mobility and spatial interaction data have become increasingly available due to the widespread adoption of location-aware technologies. Examples of mobile data include human daily activities, vehicle trajectories, and animal movements. In this study we focus on a special type of mobility data, i.e., origin–destination (OD) pairs, and propose a new adapted chord diagram plot to reveal the urban human travel spatial-temporal characteristics and patterns of a seven-day taxi trajectory data set collected in Beijing; this large scale data set includes approximately 88.5 million trips of anonymous customers. The spatial distribution patterns of the pick-up points (PUPs) and the drop-off points (DOPs) on weekdays and weekends are analyzed first. The maximum of the morning and the evening peaks are at 8:00–10:00 and 17:00–19:00. The morning peaks of taxis are delayed by 0.5–1 h compared with the commuting morning peaks. Second, travel demand, intensity, time, and distance on weekdays and weekends are analyzed to explore human mobility. The travel demand and high-intensity travel of residents in Beijing is mainly concentrated within the 6th Ring Road. The residents who travel long distances (>10 km) and for a long time (>60 min) mainly from outside the 6th Ring Road and the surrounding new towns of Beijing. The circular structure of the travel distance distribution also confirms the single-center urban structure of Beijing. Finally, a new adapted chord diagram plot is proposed to achieve the spatial-temporal scale visualization of taxi trajectory origin–destination (OD) flows. The method can characterize the volume, direction, and properties of OD flows in multiple spatial-temporal scales; it is implemented using a circular visualization package in R (circlize). Through the visualization experiment of taxi GPS trajectory data in Beijing, the results show that the proposed visualization technology is able to characterize the spatial-temporal patterns of trajectory OD flows in multiple spatial-temporal scales. These results are expected to enhance current urban mobility research and suggest some interesting avenues for future research.

Empirical Study on the Boundary Space Form of Residential Blocks Oriented Toward Low-Carbon Travel

As one of the three major carbon sources in cities, urban mobility has posed severe challenges to the social environment. Promoting low-carbon travel for residents is an important measure for building a low-carbon city and mitigating climate change. However, to date, previous research on residents’ low-carbon travel has been more oriented toward urban planning, while quantitative research on the influence of the boundary space form of residential blocks on residents’ travel modes, which takes residential blocks as the research objects at the meso- and micro-level, is relatively rare. Residential blocks in China, which were built in the late 1990s, mostly have a large and gated spatial form. Individual residential blocks are often gated by fences, commercial buildings, and other forms of interfaces, forming an independent residential group. Long and closed boundary forms will have a certain impact on residents’ choice of low-carbon travel modes, such as walking, riding bikes, and so on. Taking Nanjing as an example, this paper explores the essential factors that impact residents’ travel behaviors from the perspective of the boundary space of residential blocks, combining the socio-economic attributes of residents, land use, and transit facilities, and there are four dimensions to the study, including the boundary block scale, types of boundary interface, density and distribution of accesses, and the slow-travel environment, proposing recommended values of the relevant indicators in a targeted manner. This paper selects 21 residential blocks in the main districts in Nanjing, conducting a related survey on the residents’ socio-economic attributes and travel characteristics, boundary space form, land use, and transit facilities. The data obtained from the survey are analyzed by correlation analysis and multiple logistic regression analysis, so as to screen out the key variables of the boundary space forms of the blocks that affect residents’ low-carbon travel. Meanwhile, on the basis of the appropriate share of low-carbon travel, the unary linear regression model is used to propose ideal recommended values of the key variables of the boundary space forms of the residential blocks. For instance, the block boundary density is recommended to be above 34.38 km/km², the permeability coefficient of the block interface should be above 0.43, the commercial interface ratio should be above 18.16 km/km², the density of accesses of the blocks is recommended to be above 246.71 km/km², and the cross-sectional ratio of the slow-travel roads should be above 0.5.

Analysis of spatio-temporal distribution characteristics of passenger travel behaviour based on online ride-sharing trajectory data

With the rapid development of information technology, the travel mode of “ Internet + Transportation ” has brought convenience to residents while causing a series of urban traffic problems. Based on GPS data from Didi Chuxing, this paper relates the residents’ travel behaviour to their daily activities, and studies the regularity of the pick-up and drop-off activities of residents from the dimensions of time and space. The results are as follows: Firstly, the daily variation trend of residents’ travel is unbalanced. At the same time, the travel volume of residents is at a higher level during night. Secondly, the travel of urban residents is closely related to urban areas and urban functional structures. On the administrative scale, the distribution of residents’ travel is concentric, and the farther the distance from the downtown area is, the smaller the number of orders is. On the functional scale, the hot functional areas of residents’ travel are related to residents’ needs. At last, mastering the distribution of residents’ travel hot spots and time will help to understand the residents’ travel mode and the current situation of urban traffic, and provide new ideas for solving traffic problems in big cities.

Analysis of Urban Road Construction and Related Problems and Countermeasures

With the rapid development of China’s economy, China’s urbanizationprocess is also accelerating, the municipal road as an important supporting force to accelerate the construction of urbanization is becomingmore and more important, corresponding, the city’s requirements on theroad is also gradually improving, the quality of the city road affects thedaily travel of urban residents. If the road design is not reasonable or theconstruction quality is not up to standard, it will bring great changes tothe urban residents. Therefore, urban road construction will face greaterchallenges in the future. This paper emphasizes the importance of roadconstruction quality, analyzes some common problems in urban road construction, and puts forward some solutions to the problems in urban roadconstruction

Parameter Selection and Evaluation of Robustness of Nanjing Metro Network Based on Supernetwork

Metro plays an important role in an urban public transport system. With the use of metro lines, the increasing flow of people has brought tremendous pressure to the operation of metro lines, resulting in different faults, thus affecting the travel of residents. How to qualitatively evaluate the robustness of the metro network after encountering faults is a problem worthy of attention. By 2018, ten metro lines have been opened in Nanjing, forming a radiation structure from the urban center to the surrounding suburbs. In this study, the metro faults in Nanjing in the past three years are counted and classified. The space L and space P models for the metro network are constructed. The robustness of a Nanjing metro network is measured by the three indicators: network connectivity efficiency, largest connected subgraph size, and average subgraph size. The concept of supernetwork is proposed, and the metro line is considered as a whole. The robustness changes of the Nanjing metro network caused by the attacks on the nodes and hyperedges of the metro network are analyzed. The results show that deliberate attack causes more damage than a random attack. When traffic hub stations and trunk lines are attacked, the performance of the metro network will decline sharply. The research conclusion has certain practical value to enhance the anti-fault ability of the metro network.

Suitable Size of Block Opening Based on Network Reliability

A block road network based on urban spatial dimension was constructed to address the problems caused by increasing closed units that restrain the urban economy of a city and limit public resources and to implement a block system. Subsequently, a suitable size of block opening was determined by conducting a network reliability analysis on the block network. From the perspective of network reliability, the limitations of traditional network entropy were analyzed in the research on block road network reliability. Maximum flow entropy and shortest path entropies based on network entropy theory were introduced from the perspectives of directed and undirected road networks, the calculation method of maximum flow entropy and shortest path entropy of the block road network was provided, and a reliability rating criterion for the road network was established. The maximum flow and shortest path entropies in the block opening process were calculated under different block opening phases. Then, the reliability of each road network was determined based on the reliability criterion of road network, which is used as the basis for determining the suitable size of the block opening. An empirical analysis was conducted based on a residential area named Zhongxuanliyuan in Shanghai. The residential area networks were established from the nodes classified based on residents' life, leisure, travel, and others. The networks were divided into three stages based on the opening process, the shortest path entropy of each road network was calculated, and the suitable size of the block opening was analyzed and established. Results showed that the calculation of maximum flow and shortest path entropies can effectively determine the high-risk nodes of the block road network, and the network reliability has more advantages than the traditional network entropy. The network reliability determines the suitable size of the block opening based on the standard of block road network reliability, provides the basis for the phased block opening, and proposes new concepts and methods in conducting block system reconstruction.

Shared Subway Shuttle Bus Route Planning Based on Transport Data Analytics

The development requirements of shared buses are extremely urgent to alleviate urban traffic congestions by improving road resource utilization and to provide a neotype transportation mode with good user experiences. The key to shared bus implementation lies in accurately predicting travel requirements and planning dynamic routes. However, the sparseness and the high volatility of shared bus data bring a great resistance to accurate prediction of travel requirements. Based on the consideration of user experiences, optimization objectives of shared bus route planning are significantly different from traditional public transportation and shared bus route planning is far more challenging than online car-hailing services due to the relatively high number of passengers. In this paper, we put forward a two-stage approach (SubBus), which is composed of travel requirement prediction and dynamic routes planning, based on various crowdsourced shared bus data to generate dynamic routes for shared buses in the “last mile” scene. First, we analyze the resident travel behaviors to obtain five predictive features, such as flow, time, week, location, and bus, and utilize them to predict travel requirements accurately based on a machine learning model. Second, we design a dynamic programming algorithm to generate dynamic, optimal routes with fixed destinations for multiple operating buses utilizing prediction results based on operating characteristics of shared buses. Extensive experiments are performed on real crowdsourced shared subway shuttle bus data and demonstrate that SubBus outperforms other methods on dynamic route planning for the “last mile” scene. Note to Practitioners —This paper is inspired by the problem of shared subway shuttle bus dynamic route planning for the “last mile” scene, and it is also applicable to other scenes, including commuting scenes, urban transportation hub scenes, and destination scenes of the tourist market. Shared bus operation routes at such scenes are usually aimed at trips with fixed destinations. Existing approaches to planning routes are generally designed for traditional transportation, such as traditional buses and taxis. In this paper, we propose a novel two-stage dynamic route planning approach (SubBus) based on the operation characteristics of shared subway shuttle buses. We perform a resident travel behavior analysis to improve the accuracy of travel requirement prediction. After that, we combine the prediction results and station properties to gain shared bus optimal routes. We then display how to apply SubBus to optimize shared bus operation status based on crowdsourced shared subway shuttle bus data generated by Panda Bus Company. We keep a continuous collaboration with the company to optimize the approach details and experimental effects, which demonstrate that our approach can generate effective routes for shared subway shuttle buses to optimize operation status on the “last mile” issue.

Identifying tourists and analyzing spatial patterns of their destinations from location-based social media data

Reliable travel behavior data is a prerequisite for transportation planning process. In large tourism dependent cities, tourists are the most dynamic population group whose size and travel choices remain unknown to planners. Traditional travel surveys generally observe resident travel behavior and rarely target tourists. Ubiquitous uses of social media platforms in smartphones have created a tremendous opportunity to gather digital traces of tourists at a large scale. In this paper, we present a framework on how to use location-based data from social media to gather and analyze travel behavior of tourists. We have collected data of about 67,000 users from Twitter using its search interface for Florida. We first propose several filtering steps to create a reliable sample from the collected Twitter data. An ensemble classification technique is proposed to classify tourists and residents from user coordinates. The accuracy of the proposed classifier has been compared against the state-of-the-art classification methods. Finally, different clustering methods have been used to find the spatial patterns of destination choices of tourists. Promising results have been found from the output clusters as they reveal most popular tourist spots as well as some of the emerging tourist attractions in Florida. Performance of the proposed clustering techniques has been assessed using internal clustering validation indices. We have analyzed temporal patterns of tourist and resident activities to validate the classification of the users in two separate groups of tourists and residents. Proposed filtering, identification, and clustering techniques will be significantly useful for building individual-level tourist travel demand models from social media data.

An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors

With the large scale operation of electric buses (EBs), the arrangement of their charging optimization will have a significant impact on the operation and dispatch of EBs as well as the charging costs of EB companies. Thus, an accurate grasp of how external factors, such as the weather and policy, affect the electric consumption is of great importance. Especially in recent years, haze is becoming increasingly serious in some areas, which has a prominent impact on driving conditions and resident travel modes. Firstly, the grey relational analysis (GRA) method is used to analyze the various external factors that affect the power consumption of EBs, then a characteristic library of EBs concerning similar days is established. Then, the wavelet neural network (WNN) is used to train the power consumption factors together with power consumption data in the feature library, to establish the power consumption prediction model with multiple factors. In addition, the optimal charging model of EBs is put forward, and the reasonable charging time for the EB is used to achieve the minimum operating cost of the EB company. Finally, taking the electricity consumption data of EBs in Baoding and the data of relevant factors as an example, the power consumption prediction model and the charging optimization model of the EB are verified, which provides an important reference for the optimal charging of the EB, the trip arrangement of the EB, and the maximum profit of the electric public buses.

Spatial differentiation of daily travel carbon emissions in small- and medium-sized cities: An empirical study in Kaifeng, China

Abstract Issues relating to low-carbon transportation have received increasing attention in recent years for two competing reasons: the effort to slow down global warming and the upward trend in private vehicle ownership in China. Residential carbon emissions and the mechanisms influencing them have been adequately addressed by the published literature. However, both the built environment and socio-economic factors are spatially heterogeneous within cities, and this can be expected to lead to a corresponding spatial heterogeneity in residential carbon emissions. To better characterize spatial heterogeneity in the mechanisms influencing carbon emissions, this study collected fine-scale spatially-distributed data pertaining to carbon emissions arising from the travel activities of residents in Kaifeng City. The spatial characteristics of and factors influencing household travel carbon emissions were then investigated using spatial auto-correlation analysis and the geographical weighted regression (GWR) method. The results show that areas with high values of household travel carbon emissions were primarily in the new urban development zone and in the more rapidly-expanding built-up areas in the outer layer of the city. Additionally, a large average distance between working and living locations in the newly-built commercial housing communities in the rapidly urbanizing areas is a leading factor increasing resident travel carbon emissions. Meanwhile, the long average distances traveled for schooling on account of the currently imbalanced development of education resources is another leading factor for increasing residential travel carbon emissions. The conclusions of this research elucidate the spatial heterogeneity in the mechanisms influencing residential travel carbon emissions within the city. In particular, this study confirms that urban sprawl is a leading factor in driving residential travel behavior and thus carbon emissions. More importantly, this study demonstrates that the documented inequality in resident travel-related carbon emissions does not result from economic (e.g., household income) inequality. Instead, it is caused by the existence of inequality in the allocation of socioeconomic infrastructure (e.g., schools). It is our expectation that the results of this study can be used as a valuable reference for planning low-carbon cities.

Measurements of Pedestrian Friendliness of Residential Area: A Case Study in Hexi District of Nanjing

As part of urban transportation, walking plays an important role in promoting the development of urban green transportation systems. Through the analysis of resident travel preference and the influence factors on travel choice of land use and related effects, this paper puts forward the walking accessibility, pedestrian route directness (PRD), street walking popularity and the ratio of green interface as a quantitative index of the measurement system of pedestrian friendliness, to evaluate the effect of land use and urban space on the residents’ walking. Based on this, this paper takes Nanjing Hexi District as an example, using ArcGIS software to quantify the city residents’ travel purpose. Two-dimensional space characteristic analysis verifies that the high density and high degree of mixing, high accessibility blocks with good street environmental quality and open environment on the pedestrian block has a good role in promoting walking, and then puts forward optimization suggestions to promote the pedestrian friendliness in Hexi District.

Hierarchical hub location model and hybrid algorithm for integration of urban and rural public transport

Integration of urban and rural infrastructure is critical to integrating urban and rural public transport. A public transport hub is an important element of infrastructure, and it is the key facilities that serve as transferring points between cities and towns. The location of hub is related to the convenience of travel for urban and rural residents and the closeness of economic interactions between urban and rural areas. In this article, considering the background of the integration of urban and rural public transport, from the perspective of public transport hubs in urban and central town, a multi-level hub-and-spoke network is designed, and the location of integration of urban and rural public transport hub is determined. Based on the connection associated with central towns and the capacity constraints of hubs and to achieve the minimum total cost, this article proposes a mixed-integer programming model that employs a genetic and tabu search hybrid optimization algorithm to validate and analyze, which used the urban and rural public transport data from a specified area of Shandong province in China. The results indicate that the model can simultaneously determine locations for hubs in cities and central towns while minimizing total cost. The hub capacity constraint significantly influences the location of two-level hubs. The hub capacity constraint in the model can reduce the transportation cost for an entire network and optimize the transportation network. This study on urban and rural public transport hub location in a hub-and-spoke network not only reduces the transportation cost of the network but also completes and supplements the location theory of integration of urban and rural public transport.