Cycling speed is an important attribute of bicycle traffic flow, being related to travel times, safety and road capacity. Although cycling speed changes constantly during a trip, it is typically measured at the trip-average or aggregated level, and microscopic speed fluctuations are rarely studied. This study aims to quantitatively understand the cycling speed stability within a trip and the determinants of speed stability and disruption. To this end, data from bicycle trips tracked with GPS devices are used. A change point detection method, the pruned exact linear time (PELT) algorithm, is adapted to split trip trajectories into segments differing in speed stability. Then, a rule-based algorithm is developed to classify segments into six speed (in)stability patterns: stable, increase, decrease, V-shape (speed decreases followed by increases), reverse V-shape (speed increases followed by decreases) and complicated unstable patterns. Finally, a two-level multinomial model is estimated to examine the determinants of different patterns. The findings suggest that stable patterns account for half the trip distances, and their speed is higher than the speed of unstable patterns. The V-shape pattern is the most frequent unstable type. Intersections, turns and built-up land use are the main causes of unstable speeds. Cycling on physically separate paths tends to involve more unstable speeds than on mixed-use infrastructures, such as bicycle streets and bicycle tracks. This study finds that daily cycling involves a considerable amount of unstable speed. While its effects have not been directly examined, speed instability likely increases travel times and physical effort and is perceived negatively by cyclists. This underscores the potential benefits of a smooth cycling network and highlights the need for future research on the role of speed stability.

Smooth cycling can improve the competitiveness of bicycles. Understanding cycling speed variation during a trip reveals the infrastructure or situations which promote or prevent smooth cycling. However, research on this topic is still limited. This study analyses speed variation based on data collected in the Netherlands, using GPS-based devices, continuously recording geographical positions and thus the variation in speeds during trips. Linking GPS data to spatial data sources adds features that vary during the trip. Multilevel mixed-effects models were estimated to test the influence of factors at cyclist, trip and tracking point levels. Results show that individuals who prefer a high speed have a higher average personal speed. Longer trips and trips made by conventional electric bicycles and sport bicycles have a higher average trip speed. Tracking point level variables explain intra-trip cycling speed variations. Light-medium precipitation and tailwind increase cycling speed, while both uphill and downhill cycling is relatively slow. Cycling in natural and industrial areas is relatively fast. Intersections, turns and their adjacent roads decrease cycling speed. The higher the speed, the stronger the influence of infrastructure on speed. Separate bicycle infrastructure, such as bike tracks, streets and lanes, increase speed. These findings are useful in the areas of cycling safety, mode choice models and bicycle accessibility analysis. Furthermore, these findings provide additional evidence for smooth cycling infrastructure construction.

Travel-related attitudes are believed to affect the connections between the built environment and travel behaviour. Previous studies found supporting evidence for the residential self-selection hypothesis which suggests that the impact of the built environment on travel behaviour could be overestimated when attitudes are not accounted for. However, this hypothesis is under scrutiny as the reverse causality hypothesis, which implies a reverse direction of influence from the built environment towards attitudes, is receiving increased attention in recent research. This study tests both directions of influence by means of cross-sectional and longitudinal structural equation models. GPS tracking is used to assess changes in travel behaviour in terms of car kilometres travelled. The outcomes show stronger reverse causality effects than residential self-selection effects and that land-use policies significantly reduce car kilometres travelled. Moreover, the longitudinal models show that the built environment characteristics provide a better explanation for changes in car kilometres travelled than the travel-related attitudes. This contradicts the cross-sectional analysis where associations between car kilometres travelled and travel-related attitudes were stronger. This highlights the need for more longitudinal studies in this field.

Travel-related attitudes and dissonance between attitudes and the characteristics of the residential built environment are believed to play an important role in the effectiveness of land use policies that aim to influence travel behaviour. To date, research on the nature and directions of causality of the links between these variables has been hindered by the lack of longitudinal approaches. This paper takes such an approach by exploring how people across different population groups adjust their residential environments and attitudes over time. Two latent class transition models are used to segment a population into consonant and dissonant classes to reveal differences in their adjustment process. Interactions between (1) the distance to railway stations and travel-mode-related attitudes and (2) the distance to shopping centres and the importance of satisfaction with these distances are modelled. The models reveal mixed patterns in consonant and dissonant classes at different distances from these destinations. These patterns remain relatively stable over time. People in more dissonant classes generally do not have a higher probability of switching to more consonant classes. People adjust their built environments as well as their attitudes over time and these processes differ between classes. Implications for policies are discussed.

As people require time to adjust their travel behaviour to changes in residential location and transport infrastructure, there is a need for long-term empirical studies quantifying the relationships between locations, individuals and travel behaviour. Such empirical evidence is critical for assessing previous and candidate future land use-transport policies. Existing research however, has mostly investigated travel behaviour during relatively short time periods and for a single transport mode. This paper examines the development of travel behaviour and its socio-demographic and location determinants, using Dutch National Travel Survey data from 1980 to 2010 among other sources, for the Randstad, the Netherlands. A pseudo panel analysis is conducted to investigate the effect of various indicators on average daily distance travelled by train, car and bicycle over three decades. Econometric models including pooled ordinary least squares, fixed and random effects and a hybrid model were tested to identify the best fit. The results indicate that average daily distance travelled rose until the mid-1990s before witnessing a decrease till 2010. Interestingly, half of the Randstad inhabitants have been travelling ≤26 km per day over the past thirty years. Furthermore, as people grow older, they increasingly travel more by train and bicycle. Finally, a rise in suburban inhabitants decreases the average distance travelled by train and increases that of bicycle, while a rise in rural inhabitants encourages higher distances travelled by car.

Transport infrastructure plays a fundamental part in the development of cities and regions. Important transport routes generate substantial development pressure. In the past, city centres had the strongest effect on the location of activities but more recently there is the view that accessibility to the motorway system is more important for some employment location decisions. Various types of employment sectors can now be found in clusters, often close to transport infrastructure, despite technological developments that theoretically make proximity less important (see for example Forkenbrock and Foster, 1996; Krugman, 1991; Quigley, 1998; Scott, 1998).

City and regional governments in North America and the Netherlands are implementing transit-oriented development (TOD) policies to provide residents with accessible and compact communities that are socially, environmentally, and economically sustainable. Through 13 in-depth semi-structured interviews with planners and transportation professionals in the United States, Canada, and the Netherlands, this study attempts to identify the factors that practitioners in these regions determine to be essential for the post-development success of TOD. Our analysis reveals that seven key elements contribute to the success of TOD which are approached differently by planners in the three regions. The study concludes by suggesting ways in which professionals could integrate land use and transportation projects based on planning for flexibility, accessibility, and collaboration.

Improvements in geographical information systems, the wider availability of high-resolution digital data and more sophisticated econometric techniques have all contributed to increasing academic interest and activity in long-term impacts of transport infrastructure networks (TINs) on land use (LU). This paper provides a systematic review of recent empirical evidence from the USA, Europe and East Asia, classified regarding the type of transport infrastructure (road or rail), LU indicator (land cover, population or employment density, development type) and outcome (significance, relationship’s direction) as well as influential exogenous factors. Proximity to the rail network is generally associated with population growth (particularly soon after the development of railway infrastructure), conversion to residential uses and the development of higher residential densities. Meanwhile, proximity to the road network is frequently associated with increases in employment densities as well as the conversion of land to a variety of urban uses including commercial and industrial development. Compared with road infrastructure, the impact of rail infrastructure is often less significant for land cover or population and employment density change. The extent of TINs’ impact on LU over time can be explained by the saturation in TIN-related accessibility and LU development.

The influence of the built environment on travel behaviour and the role of intervening variables such as socio-demographics and travel-related attitudes have long been debated in the literature. To date, most empirical studies have applied cross-sectional designs to investigate their bidirectional relationships. However, these designs provide limited evidence for causality. This study represents one of the first attempts to employ a longitudinal design on these relationships. We applied cross lagged panel structural equation models to a two-wave longitudinal dataset to assess the directions and strengths of the relationships between the built environment, travel behaviour and travel-related attitudes. Results show that the residential built environment has a small but significant influence on car use and travel attitudes. In addition, the built environment influenced travel-related attitudes indicating that people tend to adjust their attitudes to their built environment. This provides some support for land use policies that aim to influence travel behaviour.