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BackgroundMany regions worldwide are struggling to create a mode shift from private cars to more sustainable transport modes. While there are many reviews regarding travellers’ preferences and travel mode choices, there is a lack of an updated review that provides a comprehensive overview of the factors that make public transport systems attractive.AimThis review aims to fill the knowledge gap by offering insights into the factors influencing travel behaviour and the demand for public transport. It has two primary objectives: • Summarize general conclusions drawn from international literature reviews. • Present specific insights on the topic pertaining to the Nordic countries. To the best of our knowledge, this is the first review with a Nordic focus regarding public transport preferences and travel mode choices. The special focus on these countries is motivated by their relatively more ambitious policies for reducing emissions in the transport sector compared to many other countries, and their relatively high usage of public transport today.MethodsTo achieve these objectives, we conducted a review of existing literature. This review encompassed international literature reviews and included an examination of results from the Nordic countries.FindingsThe findings show that reliability and frequency are important factors for creating an attractive public transport supply. However, there is only limited evidence regarding the impact of improvements in these attributes on public transport demand, so this needs more research. This review highlights the importance of understanding the underlying motivations for travel mode choice and provides recommendations on areas for further investigation to understand the attractiveness of public transport supply.

In recent years, the e-bike has become increasingly popular in many European countries. With higher speeds and less effort needed, the e-bike is a promising mode of transport to many, and it is considered a good alternative for certain car trips by policy-makers and planners. A major limitation of many studies that investigate such substitution effects of the e-bike, is their reliance on cross-sectional data which do not allow an assessment of within-person travel mode changes. As a consequence, there is currently no consensus about the e-bike’s potential to replace car trips. Furthermore, there has been little research focusing on heterogeneity among e-bike users. In this respect, it is likely that different groups exist that use the e-bike for different reasons (e.g. leisure vs commute travel), something which will also influence possible substitution patterns. This paper contributes to the literature in two ways: (1) it presents a statistical analysis to assess the extent to which e-bike trips are substituting trips by other travel modes based on longitudinal data; (2) it reveals different user groups among the e-bike population. A Random Intercept Cross-Lagged Panel Model is estimated using five waves of data from the Netherlands Mobility Panel. Furthermore, a Latent Class Analysis is performed using data from the Dutch national travel survey. Results show that, when using longitudinal data, the substitution effects between e-bike and the competing travel modes of car and public transport are not as significant as reported in earlier research. In general, e-bike trips only significantly reduce conventional bicycle trips in the Netherlands, which can be regarded an unwanted effect from a policy-viewpoint. For commuting, the e-bike also substitutes car trips. Furthermore, results show that there are five different user groups with their own distinct behaviour patterns and socio-demographic characteristics. They also show that groups that use the e-bike primarily for commuting or education are growing at a much higher rate than groups that mainly use the e-bike for leisure and shopping purposes.

Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger spatiotemporal coverage of the population and its mobility. However, ground truth information such as trip origins and destinations, travel modes, and trip purposes are not included by default. Such important attributes must be imputed to maximize the usefulness of the data. This paper targets at studying the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning models. A labeled MDLD dataset with ground truth information is used to train the proposed models, resulting in a 95% recall rate in identifying trip ends and over 93% tenfold cross-validation accuracy in imputing the five travel modes (drive, rail, bus, bike and walk) with a random forest (RF) classifier. The proposed framework is then applied to two large-scale MDLD datasets, covering the Baltimore-Washington metropolitan area and the United States, respectively. The estimated trip distance, trip time, trip rate distribution, and travel mode share are compared against travel surveys at different geographies. The results suggest that the proposed framework can be readily applied in different states and metropolitan regions with low cost in order to study multimodal travel demand, understand mobility trends, and support decision making.

As an important indicator of urban development capacity, vitality can be affected by the human perception of street views, which is a dynamic sensory process that can differ greatly according to different transportation modes, due to their different travel speeds, distances, and routes. However, few studies have evaluated how the dynamic spatial perceptions differ between different travel modes and how these differences can affect vitality differently, due to the limitation of city-scale quantitative data on the dynamic perception of urban scenes. To fill the gap, we propose a “dynamic through-movement perception” (DTMP) measure which integrates a streetscape quality evaluation model with a network-based movement potential model. We measure the streetscape qualities from Baidu street-view images (SVI) and compare the spatial perceptions of drivers and pedestrians in central Guangzhou, China. First, more than twenty visual elements were classified from SVIs to predict human perceptions collected from visual surveys. Second, the through-movement probability of driving and walking were calculated based on classic natural movement theory in space syntax and measured as the angular betweenness for the two travel modes. Third, we accumulate the multipliers of visual perception and through-movement probability of driving and walking as the DTMP for both modes. Lastly, the DTMPs of both modes were fitted into linear regression models to explain street vitality, which is measured using Baidu mobile phone check-in data, when other control variables such as functional density, functional diversity and amenity clustering reachability are accounted for. The results show that the dynamic perception of driving overall shows a stronger correlation with street vitality, while perceived richness is significantly positive in both travel modes. This study provides the first quantitative evidence to reveal how the movement probability of different travel modes can significantly influence people’s sense of place, while in turn increasing street vitality. Our results can explain how different types of street commerce (i.e., pedestrian-oriented, and auto-oriented) aggregate spontaneously due to the dynamic movement potential, which provides an important reference for urban planners and decision makers for improving street vitality when making urban revitalization policies.

The issue of the effects of travel on subjective well-being (SWB) has recently attracted increasing interest in transport studies. A common finding is that travel affects SWB indirectly through out-of-home activities. However, little is known about how to operationalize this relationship. In this study, we proposed a conceptual model and estimated structural equation models relating travel mode use and activities with multiple SWB dimensions, including affective components (positive affect and negative affect) and cognitive components (belongingness, achievement, and confidence in coping). We used data from a national mobility project in Japan (N = 13,000) to estimate the postulated models. We found that while public transport use enhanced the cognitive components of SWB, it also had a negative effect on the affective components of SWB. Car use affected SWB in a more complex pattern; it promoted SWB by enabling leisure activities but also reduced SWB dimensions of belongingness and achievement through shopping activities. Active travel modes did not have a clear effect on SWB; for example, walking to school was associated with increased belongingness, whereas walking for shopping negatively influenced belongingness. Other contributing factors, such as COVID-19 worry, car access, and the ‘going-out’ problem-solving style, were also found to influence multiple dimensions of SWB. Overall, our study showed how the effects of activities on multiple dimensions of SWB varied with different travel modes, thereby revealing the indirect effect of travel mode use on SWB via activities. Suggestions for shaping transport policies towards SWB are also discussed.

A considerable number of empirical studies have explored the effects of information & communication technologies (ICT) on travel in recent years. In particular, the most attention has been paid to whether the use of ICT increases or decreases trip frequency (i.e., substitution or complementarity effects). However, the subject of whether or how travel distance and mode choice are altered by ICT (i.e., modification effects) has almost been ignored. Against this background, using data collected in Beijing, China, this paper aims to explore how purchasing intangible services (e.g., eating out at restaurants, hairdressing, and visits to zoos and movie theatres) online alters the distance and mode choice of the travel to consume these services. The results suggest that due to online purchases of intangible services, people tend to travel farther to consume these services. Consequently, 25.4% of online buyers change their travel mode choices from walking or cycling (i.e., nonmotorized modes) to public transit, private cars, or taxis (i.e., motorized modes). These findings confirm the existence of modification effects of ICT on travel. Additionally, a stepwise multinomial logistic regression model and a stepwise binomial logistic regression model are used to detect the factors influencing changes in travel distance and mode choices, respectively. The regression outcomes suggest that people who have lower living costs or feel more satisfied with online purchases are more likely to increase their travel distances and to change from nonmotorized modes to motorized modes.

Limited studies focus on educational equity from the spatial accessibility perspective. This study combines survey data and big data and proposes a multi-mode Huff two-step floating catchment area (MMH2SFCA) method to calculate accessibility while considering multiple travel modes and school attractiveness. This method can also calculate education quality by extending the accessibility in each community. Results show that our proposed method can reliably identify the accessibility differences of schools across communities. The case study indicates an inequitable distribution of educational accessibility and quality. The communities with high accessibility are concentrated in the urban center and exurban zones surrounding schools, whereas high-quality areas are mainly concentrated in the urban center. Correlation analysis suggests that the educational quality of communities with high accessibility is not always high. The findings of this study can provide improvement for accessibility measurements and help explore a new research perspective for educational equity research.

Urban populations are exposed to a multitude of traffic-related air pollutants during daily commutes. This study assessed commuters’ exposure to volatile organic compounds (VOCs) during bus, motorcycle, and bicycle commuting, and estimated the VOC inhalation dose. Benzene, toluene, ethylbenzene, and xylene (BTEX) were the main compounds detected, contributing 58 − 68% to ΣVOC (sum of the concentrations of all detected VOCs) in different travel modes. The mean ΣVOC exposure concentration was higher for motorcyclists than for cyclists and bus commuters. However, due to cyclists’ higher minute ventilation rates and longer exposure time, they had the highest ΣVOC inhalation dose based on both travel time (7.09 ± 2.36 μg min −1 ) and distance (32.9 ± 10.8 μg km −1 ). Among the three travel modes, bus commuters had the lowest ΣVOC inhalation dose based on travel time (2.33 ± 1.18 μg min −1 ) and distance (8.91 ± 4.91 μg km −1 ), while motorcyclists had a moderate ΣVOC inhalation dose based on travel time (5.08 ± 1.46 μg min −1 ) and distance (13.4 ± 5.5 μg km −1 ). Health impact assessment of VOCs showed that cyclists faced the highest carcinogenic and non-carcinogenic risks, while bus commuters experienced the lowest health risk associated with VOC exposure. Our findings underscore the need to consider air quality in transportation infrastructure design and prioritize interventions to safeguard urban commuters’ health, particularly cyclists, who are the most vulnerable to the adverse effects of traffic-related air pollutants.

Travel mode recognition is a key issue in urban planning and transportation research. While traditional travel surveys use manual data collection and have limited coverage, poor timeliness, and insufficient sample capacity, recent advancements in Global Positioning System (GPS) technology allow large-scale data collection and offer novel opportunities to enhance travel mode recognition. However, existing studies often neglect regular differences and changes in motion states across different travel modes and fail to fully integrate multi-scale spatio-temporal features, which limits the accurate classification of travel modes. To fill this gap, this study proposes a multi-scale spatio-temporal attribute fusion (MSAF) model for precise travel mode identification using solely GPS trajectories without altering their sampling rate. The MSAF model segments GPS trajectories into various temporal and spatial scales, extracting local motion states and spatial features at multiple scales. The spatio-temporal feature extraction module is constructed to extract local motion states and capture spatio-temporal dependencies. Additionally, the model incorporates a multi-scale feature fusion module, which effectively combines features of various scales through a series of fusion techniques to obtain a comprehensive representation, enabling automatic and accurate travel mode identification. Experiments on real-world datasets, including the GeoLife Trajectories dataset and the Sussex-Huawei Locomotion-Transportation (SHL) dataset, demonstrate the effectiveness of the MSAF model, achieving a competitive accuracy of 95.16% and 91.70%. This represents an improvement of 2.50% to 7.95% and 0.8% to 6.62% over several state-of-the-art baselines, effectively addressing sample imbalance challenges. Moreover, the experiments demonstrate the significant role of multiscale feature fusion in improving model performance.

After the rapid expansion of the subway system over the past two decades, some cities are preparing to build more suburban railways. The emergence of suburban railways is bound to change the choice of suburban passenger transportation. This paper studies the factors that affect the choice of travel mode at the construction stage of suburban railways, aiming to design a more rational suburban railway network and urban public transport service system. Taking Shanghai as an example, this study first surveyed revealed preference (RP) and stated preference (SP) among urban-suburban travelers. Then, we used discrete choice models (DCM) and machine learning algorithms to build a travel mode choice model based on data collection and analysis. Furthermore, the importance of each factor was analyzed, and the effects were predicted under several traffic demand management schemes. Finally, this study proposed some strategies for increasing the share of public transport. On the one hand, it is suggested that Shanghai should continue to develop suburban railways and maintain low pricing of public transport services. Considering the construction and operation costs, the government needs to provide certain subsidies to stabilize prices. On the other hand, as passengers are very sensitive to the “last mile” trips in their suburban railway travel, transport planners should strengthen the connection from and to the suburban railway stations by developing services such as shared bikes and shuttle buses. In addition, the results indicated that some traffic demand management measures can also contribute to a larger share of public transport.