Where are the dangerous intersections for pedestrians and cyclists: A colocation-based approach

Pedestrians and cyclists are vulnerable road users. They are at greater risk for being killed in a crash than other road users. The percentage of fatal crashes that involve a pedestrian or cyclist is higher than the overall percentage of total trips taken by both modes. Because of this risk, finding ways to minimize problematic street environments is critical. Understanding traffic safety spatial patterns and identifying dangerous locations with significantly high crash risks for pedestrians and cyclists is essential in order to design possible countermeasures to improve road safety. This research develops two indicators for examining spatial correlation patterns between elements of the built environment (intersections) and crashes (pedestrian- or cyclist-involved). The global colocation quotient detects the overall connection in an area while the local colocation quotient identifies the locations of high-risk intersections. To illustrate our approach, we applied the methods to inspect the colocation patterns between pedestrian- or cyclist-vehicle crashes and intersections in Houston, Texas and we identified among many intersections the ones that significantly attract crashes. We also scrutinized those intersections, discussed possible attributes leading to high colocation of crashes, and proposed corresponding countermeasures.     

Where do cyclists ride? A route choice model developed with revealed preference GPS data

To better understand bicyclists’ preferences for facility types, GPS units were used to observe the behavior of 164 cyclists in Portland, Oregon, USA for several days each. Trip purpose and several other trip-level variables recorded by the cyclists, and the resulting trips were coded to a highly detailed bicycle network. The authors used the 1449 non-exercise, utilitarian trips to estimate a bicycle route choice model. The model used a choice set generation algorithm based on multiple permutations of path attributes and was formulated to account for overlapping route alternatives. The findings suggest that cyclists are sensitive to the effects of distance, turn frequency, slope, intersection control (e.g. presence or absence of traffic signals), and traffic volumes. In addition, cyclists appear to place relatively high value on off-street bike paths, enhanced neighborhood bikeways with traffic calming features (aka “bicycle boulevards”), and bridge facilities. Bike lanes more or less exactly offset the negative effects of adjacent traffic, but were no more or less attractive than a basic low traffic volume street. Finally, route preferences differ between commute and other utilitarian trips; cyclists were more sensitive to distance and less sensitive to other infrastructure characteristics for commute trips.     

Assessing urban sidewalk networks based on three constructs: a synthesis of pedestrian level of service literature

ABSTRACT

Pedestrian Level of Service (PLOS) models are widely used to assess walking facilities. These models have been in existence since the 1970s, wherein the process broadly consists of three steps, i.e. attribute selection, model calibration, and classification of model results into service-level categories, based on Measures of Effectiveness (MOEs). This paper reviews existing sidewalk PLOS studies based on their association with the three constructs of flow characteristics, built environment and users’ perception, which in combination represents the entire walking environment spectrum, as has been indicated by existing researchers. Forty-seven PLOS studies, along with eight review papers, written by authors from the Americas, Europe, Asia and Australia, between the years of 1971 and 2019, are analysed in this review. The review finds that although 49% of the studies employed both qualitative and quantitative data for their respective methodologies, but none of them use all the three broad constructs in a combined fashion. Also, in selecting the attributes to be used for developing the PLOS, these studies have only referred to previous literature available at that point in time, and not employed any consistent and robust method in selecting context-specific attributes. When it came to the preferred analysis technique, 60% of the studies favoured the use of the regression technique while calibrating their model, whereas 22% used a points-based marking scheme. Finally, 89% of the studies manually classifies the PLOS model results to respective service levels (i.e. letter grades), as opposed to utilising a classification algorithm. In addition, this review could identify only one paper that describes a PLOS based on pedestrian route directness, which is a measure of pedestrian network connectivity. In view of these findings, the review paper suggests the need of a robust methodology in selection of attributes and the use of innovative modelling techniques, both of which could allow the utilisation of all three constructs. Also, such advanced modelling techniques could bypass the need for categorising service levels manually. Finally, the study advocates the use of network connectivity measures in developing sidewalk PLOS, as it is an important part of the built environment.     

The Influences of the Built Environment and Residential Self-Selection on Pedestrian Behavior: Evidence from Austin, TX

Pedestrian travel offers a wide range of benefits to both individuals and society. Planners and public health officials alike have been promoting policies that improve the quality of the built environment for pedestrians: mixed land uses, interconnected street networks, sidewalks and other facilities. Whether such policies will prove effective remains open to debate. Two issues in particular need further attention. First, the impact of the built environment on pedestrian behavior may depend on the purpose of the trip, whether for utilitarian or recreational purposes. Second, the connection between the built environment and pedestrian behavior may be more a matter of residential location choice than of travel choice. This study aims to provide new evidence on both questions. Using 1368 respondents to a 1995 survey conducted in six neighborhoods in Austin, TX, two separate negative binomial models were estimated for the frequencies of strolling trips and pedestrian shopping trips within neighborhoods. We found that although residential self-selection impacts both types of trips, it is the most important factor explaining walking to a destination (i.e. for shopping). After accounting for self-selection, neighborhood characteristics (especially perceptions of these characteristics) impact strolling frequency, while characteristics of local commercial areas are important in facilitating shopping trips.     

Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application

Pedestrians and cyclists are amongst the most vulnerable road users. Pedestrian and cyclist collisions involving motor-vehicles result in high injury and fatality rates for these two modes. Data for pedestrian and cyclist activity at intersections such as volumes, speeds, and space–time trajectories are essential in the field of transportation in general, and road safety in particular. However, automated data collection for these two road user types remains a challenge. Due to the constant change of orientation and appearance of pedestrians and cyclists, detecting and tracking them using video sensors is a difficult task. This is perhaps one of the main reasons why automated data collection methods are more advanced for motorized traffic. This paper presents a method based on Histogram of Oriented Gradients to extract features of an image box containing the tracked object and Support Vector Machine to classify moving objects in crowded traffic scenes. Moving objects are classified into three categories: pedestrians, cyclists, and motor vehicles. The proposed methodology is composed of three steps: (i) detecting and tracking each moving object in video data, (ii) classifying each object according to its appearance in each frame, and (iii) computing the probability of belonging to each class based on both object appearance and speed. For the last step, Bayes’ rule is used to fuse appearance and speed in order to predict the object class. Using video datasets collected in different intersections, the methodology was built and tested. The developed methodology achieved an overall classification accuracy of greater than 88%. However, the classification accuracy varies across modes and is highest for vehicles and lower for pedestrians and cyclists. The applicability of the proposed methodology is illustrated using a simple case study to analyze cyclist–vehicle conflicts at intersections with and without bicycle facilities.     

Effects of built environment on walking at the neighbourhood scale. A new role for street networks by modelling their configurational accessibility?

The hypothesis of this paper is that some features of the built environment, particularly those concerned with the accessibility of the street network, could be associated with the proportion of pedestrians on all trips (modal split) found in different parts of a city. Quantitative analysis (bi-variate correlation and a multiple regression model) was used to establish the association between variables. The study area covered a substantial part of the metropolitan area in Madrid, Spain. Results showed a consistent influence of five particular indexes in the multi-variate model. Not surprisingly for this kind of research, four of them described density and mix of land uses. But perhaps more interestingly, the first one was a measure of the accessibility of the public space network, a less prominent variable in literature to date. This variable is called herein configurational accessibility, calculated using Space Syntax, an urban morphology theory. The relevance of configurational accessibility is probably related to its surprising ability to synthesize global and perceived properties of street networks at the same time. The findings introduce the idea that the configuration of the urban grid can influence the proportion of pedestrians (as a part of total trips in any transport mode) who choose to walk on single-journey trips. The discussion links with the current debate about walkability indexes and the need of empirical support for the chosen variables and also with transport planning. Because the relevance of the street network’s role is not so easy to grasp, inputs from configurational theory and the pedestrian potential underlying this fact are also discussed at the end of the paper.     

Survey of pedestrians' crossing time at non‐signalized mid‐block street crossing

At non‐signalized mid‐block street crossings in China's cities, pedestrians often weave between motor vehicle flows. This paper investigated the influence patterns of the gender and age of pedestrians, the presence of a pedestrian group, vehicles' interference and the crossing direction on the crossing time at non‐signalized mid‐block street crossings in Changsha, China. The results show that the crossing speed is approximately 1–1.1 m/s; the crossing time increases with increasing age, and the crossing speed of a pedestrian will be quicker when the time gap between the pedestrian and the oncoming vehicle is smaller if he/she decides to cross. This paper also analyzed the crossing time pattern when pedestrians cross lane by lane and found that pedestrians spend the most time crossing the first lane and the least time crossing the middle lane, regardless of whether they are crossing from the curb to the central island or from the central island to the curb. The crossing speed is an important input to the design of pedestrian facilities, so these findings can be applied to the assessment of pedestrian crossing safety in China's cities and can provide a basis for the design of pedestrian crossing facilities. Copyright © 2017 John Wiley & Sons, Ltd.     

Bicyclist commuters’ choice of on-street versus off-street route segments

When using limited funds on bicycle facilities, it would be helpful to know the extent to which a new facility will be used. If a bicycle lane is added to a street, how many bicyclists will no longer use the adjacent sidewalk? If a separate bicycle path is constructed, how many bicyclists will move from the street or sidewalk? This study seeks to identify factors that explain a bicyclist’s choice between available facility choices—off-street (sidewalk and bicycle path) or on-street (bicycle lane and roadway). This paper investigates these issues through a survey of bicyclists headed to Purdue University in West Lafayette, IN, USA. The first data collected to address these questions were “site-based”. Bicyclists were interviewed on campus at the end of their trips and asked which part of the cross-sections along their routes they had used—on-street or off-street. The characteristics of a particular cross-section of street right-of-way were then compared against the characteristics of each bicyclist and his/her observed choice of street, sidewalk, lane, or path. Later, “route-based” serial data were also added. The study developed a mixed logit model to analyze the bicyclists’ facility preferences and capture the unobserved heterogeneity across the population. Effective sidewalk width, traffic signals, segment length, road functional class, street pavement condition, and one-way street configuration were found to be statistically significant. A bicycle path is found to be more attractive than a bicycle lane. Predictions from the model can indicate where investments in particular bicycle facilities would have the most desirable response from bicyclists.     

A robust approach for road users classification using the motion cues

Video monitoring of traffic is a common practice in major cities. The data generated by video monitoring has practical uses such as traffic analysis for city planning. However, the usefulness of video monitoring of traffic is limited unless there is also a reliable way to automatically classify road users. This paper presents an automated method of road users’ classification into vehicles, cyclists, and pedestrians by using their motion cues. In this method, the movement of road users was captured on sequences of video frames. The videos were analysed using a feature-based tracking system, which has returned the tracks of road users. The separate pieces of information gained from these tracks are hereafter called Classifiers. There are nineteen classifiers included in this method. The classifiers’ values were assessed and integrated into a fuzzy membership framework, which in turn required prior configurations to be available. This led to the final classification of road users. The performance of this method demonstrated promising results. An important contribution of this paper is the creation of a robust approach that can integrate different classifiers using fuzzy membership framework. The developed method also uses parametric classifiers, which do not depend on the specific geometry or traffic operation of the intersection. This is a key advantage because it enables transferability and improves the practicality and usefulness of the method.     

Assessment of antenna characteristic effects on pedestrian and cyclists travel-time estimation based on Bluetooth and WiFi MAC addresses

Monitoring pedestrian and cyclists movement is an important area of research in transport, crowd safety, urban design and human behaviour assessment areas. Media Access Control (MAC) address data has been recently used as potential information for extracting features from people’s movement. MAC addresses are unique identifiers of WiFi and Bluetooth wireless technologies in smart electronics devices such as mobile phones, laptops and tablets. The unique number of each WiFi and Bluetooth MAC address can be captured and stored by MAC address scanners. MAC addresses data in fact allows for unannounced, non-participatory, and tracking of people. The use of MAC data for tracking people has been focused recently for applying in mass events, shopping centres, airports, train stations, etc. In terms of travel time estimation, setting up a scanner with a big value of antenna’s gain is usually recommended for highways and main roads to track vehicle’s movements, whereas big gains can have some drawbacks in case of pedestrian and cyclists. Pedestrian and cyclists mainly move in built distinctions and city pathways where there is significant noises from other fixed WiFi and Bluetooth. Big antenna’s gains will cover wide areas that results in scanning more samples from pedestrians and cyclists’ MAC device. However, anomalies (such fixed devices) may be captured that increase the complexity and processing time of data analysis. On the other hand, small gain antennas will have lesser anomalies in the data but at the cost of lower overall sample size of pedestrian and cyclist’s data. This paper studies the effect of antenna characteristics on MAC address data in terms of travel-time estimation for pedestrians and cyclists. The results of the empirical case study compare the effects of small and big antenna gains in order to suggest optimal set up for increasing the accuracy of pedestrians and cyclists’ travel-time estimation.     

Roadway and traffic characteristics for bicycling

The promotion of bicycle transportation includes the provision of suitable infrastructure for cyclists. In order to determine if a road is suitable for bicycling or not, and what improvements need to be made to increase the level of service for bicycles on specific situations, it is important to know how cyclists perceive the characteristics that define the roadway environment. The present paper describes research developed to define which roadway and traffic characteristics are prioritized by users and potential users in the evaluation of quality of roads for bicycling in urban areas of Brazilian medium-sized cities. A focus group discussion identified 14 attributes representing characteristics that describe the quality of roads for bicycling in Brazilian cities. In addition, an attitude survey was applied with individuals to assess their perception on the attributes, along with the importance given to each one of them. The results were analyzed through the Method of Successive Intervals Analysis, which allows the transformation of categorical data into an interval scale. The analysis suggests that both the roadway and traffic characteristics related to segments and those related to intersections are important to the survey respondents. The five most important attributes, in their opinion, are: (1) lane width; (2) motor vehicle speed; (3) visibility at intersections; (4) presence of intersections; and (5) street trees (shading). Therefore, the research suggests that to promote bicycle use in Brazilian medium-sized cities, these attributes must be prioritized.     

An agent-based multimodal simulation model for capacity planning of a cross-border transit facility

Cross-border transit facilities constitute major public investment, and thus must serve the long-term needs of the communities, such as providing access to schools and businesses, contributing to a shared regional culture and lifestyle, fostering international trade, and supporting jobs for the region’s residents. Numerous studies have been conducted to evaluate the economic implications of vehicular flow delays at border crossings, however none of the studies focused on assessing cross-border flow of bus passengers and pedestrians. Since pedestrians are considered to be autonomous, intelligent, and perceptive, it is a challenging task to predict pedestrian movement and behavior in comparison to vehicular flows which follow a specific set of traffic rules. This paper presents a multiagent based multimodal simulation model to evaluate the capacity and performance of a cross-border transit facility. The significance of this research is the use of dynamic mode choice functionality in the model, which allows an individual person to make instantaneous choices between available modes of transportation. The scope of interest of the paper is limited to simulating access interface, circulation areas, ancillary and processing facilities. The developed model was calibrated to ensure realistic performance, and validated against specific performance criteria such as throughput per processing facility. In order to demonstrate the applicability of the developed simulation model, capacity and operational planning of a pedestrian transit facility was performed. The relative performance of alternative design or configuration was evaluated using the level of service criteria. Lastly, the effectiveness of each proposed capacity or operational improvement strategy was compared to the “do-nothing” scenario.     

Modelling shared space users via rule-based social force model

The promotion of space sharing in order to raise the quality of community living and safety of street surroundings is increasingly accepted feature of modern urban design. In this context, the development of a shared space simulation tool is essential in helping determine whether particular shared space schemes are suitable alternatives to traditional street layouts. A simulation tool that enables urban designers to visualise pedestrians and cars trajectories, extract flow and density relation in a new shared space design, achieve solutions for optimal design features before implementation, and help getting the design closer to the system optimal. This paper presents a three-layered microscopic mathematical model which is capable of representing the behaviour of pedestrians and vehicles in shared space layouts and it is implemented in a traffic simulation tool. The top layer calculates route maps based on static obstacles in the environment. It plans the shortest path towards agents’ respective destinations by generating one or more intermediate targets. In the second layer, the Social Force Model (SFM) is modified and extended for mixed traffic to produce feasible trajectories. Since car movements are not as flexible as pedestrian movements, velocity angle constraints are included for cars. The conflicts described in the third layer are resolved by rule-based constraints for shared space users. An optimisation algorithm is applied to determine the interaction parameters of the force-based model for shared space users using empirical data. This new three-layer microscopic model can be used to simulate shared space environments and assess, for example, new street designs.     

Traffic flow on pedestrianized streets

Giving pedestrians priority to cross a street enhances pedestrian life, especially if crosswalks are closely spaced. Explored here is the effect of this management decision on car traffic. Since queuing theory suggests that for a given pedestrian flux the closer the crosswalk spacing the lower the effect of pedestrians on cars, scenarios where pedestrians can cross anywhere should be best for both cars and pedestrians. This is the kind of pedestrianization studied.Analytic formulas are proposed for a pedestrianized street’s capacity, free-flow speed and macroscopic fundamental diagram. Of these, only the free-flow speed formula is exact. The analytic form of the capacity formula is inspired by analytic upper and lower bounds derived with variational theory for a version of the problem where cars are treated as a fluid. The formula is then calibrated against microscopic simulations with discrete cars. The MFD for the fluid version of the problem is shown to be concave and have a certain symmetry. These two geometrical properties, together with the formulae for capacity and free-flow speed, yield a simple approximation for the MFD.Both the capacity and MFD formulae match simulations with discrete cars well for all values of the pedestrian flux – errors for the capacity are well under 0.2% of the capacity before pedestrianization. Qualitatively, the formulas predict that the street’s capacity is inversely proportional to the square root of the pedestrian flux for low pedestrian fluxes, and that pedestrians increase the cars’ free-flow pace by an amount that is proportional to the pedestrian flux.     

Making Cycling Irresistible: Lessons from The Netherlands,Denmark and Germany

Abstract

This article shows how the Netherlands, Denmark and Germany have made bicycling a safe, convenient and practical way to get around their cities. The analysis relies on national aggregate data as well as case studies of large and small cities in each country. The key to achieving high levels of cycling appears to be the provision of separate cycling facilities along heavily travelled roads and at intersections, combined with traffic calming of most residential neighbourhoods. Extensive cycling rights of way in the Netherlands, Denmark and Germany are complemented by ample bike parking, full integration with public transport, comprehensive traffic education and training of both cyclists and motorists, and a wide range of promotional events intended to generate enthusiasm and wide public support for cycling. In addition to their many pro‐bike policies and programmes, the Netherlands, Denmark and Germany make driving expensive as well as inconvenient in central cities through a host of taxes and restrictions on car ownership, use and parking. Moreover, strict land‐use policies foster compact, mixed‐use developments that generate shorter and thus more bikeable trips. It is the coordinated implementation of this multi‐faceted, mutually reinforcing set of policies that best explains the success of these three countries in promoting cycling. For comparison, the article portrays the marginal status of cycling in the UK and the USA, where only about 1% of trips are by bike.     

Route choice of cyclists in Zurich

This paper presents the first route choice model for bicyclists estimated from a large sample of GPS observations and overcomes the limitations inherent in the generally employed stated preference approach. It employs an improved mode detection algorithm for GPS post-processing to determine trips made by bicycle, which are map matched to an enriched street network. The alternatives are generated as a random sample from an exhaustive, but constrained search. Accounting for the similarity between the alternatives with the path-size factor the MNL estimates show that the elasticity with regards to trip length is nearly four times larger than that with respect to the share of bike paths. The elasticity with respect to the product of length and maximum gradient of the route is small. No other variable describing the routes had an impact. The heterogeneity of the cyclists is captured through interaction terms formulated on their average behaviour.     

The travel and environmental implications of shared autonomous vehicles,using agent-based model scenarios

Carsharing programs that operate as short-term vehicle rentals (often for one-way trips before ending the rental) like Car2Go and ZipCar have quickly expanded, with the number of US users doubling every 1–2 years over the past decade. Such programs seek to shift personal transportation choices from an owned asset to a service used on demand. The advent of autonomous or fully self-driving vehicles will address many current carsharing barriers, including users’ travel to access available vehicles.This work describes the design of an agent-based model for shared autonomous vehicle (SAV) operations, the results of many case-study applications using this model, and the estimated environmental benefits of such settings, versus conventional vehicle ownership and use. The model operates by generating trips throughout a grid-based urban area, with each trip assigned an origin, destination and departure time, to mimic realistic travel profiles. A preliminary model run estimates the SAV fleet size required to reasonably service all trips, also using a variety of vehicle relocation strategies that seek to minimize future traveler wait times. Next, the model is run over one-hundred days, with driverless vehicles ferrying travelers from one destination to the next. During each 5-min interval, some unused SAVs relocate, attempting to shorten wait times for next-period travelers.Case studies vary trip generation rates, trip distribution patterns, network congestion levels, service area size, vehicle relocation strategies, and fleet size. Preliminary results indicate that each SAV can replace around eleven conventional vehicles, but adds up to 10% more travel distance than comparable non-SAV trips, resulting in overall beneficial emissions impacts, once fleet-efficiency changes and embodied versus in-use emissions are assessed.     

How does air pollution influence cycling behaviour? Evidence from Beijing

It is widely believed air pollution is an obstacle to cycling as it has negative effects on cyclists’ health outcomes and deteriorates their cycling experiences. However, the empirical studies investigating the impact of air pollution on cycling behaviour remains scarce. The aim of this paper is to fill the gap by looking at Beijing as a case study. The authors conducted a survey of 307 cyclists on the days with different levels of air quality in terms of concentration of PM_2.5 in 2015. The results show that in the polluted weather, those who persist in cycling are more likely to be male, over 30 years old, lower income or those who travel short distances. Specifically, female cyclists have a higher tendency to shift from cycling to public transit than the males and medium and high-income earners are more likely to shift to using a car than low income earners. The residents’ subjective perceptions of safety and comfort have major effects on their cycling behaviour. A higher perception of comfort and safety is related to a higher possibility of continuing cycling when air quality became polluted. Cycling for commuting trips is less likely to be replaced by other modes than cycling for non-commuting trips, such as shopping. Results of this study reveal that improving air quality in a metropolitan area such as Beijing has co-benefits of cycling renaissance. The huge investments into cycling infrastructure should be integrated with policies designed to create an attractive environment for cycling.     

The relation between bicycle commuting and non-work cycling: results from a mobility panel

This study aims to establish whether or not bicycle commuting and cycling for other purposes (e.g. shopping, visiting friends) are related over time. Using previously gathered panel data (the Dutch mobility panel) these relationships are revealed by (1) a series of conditional change models and (2) a latent transition model. The conditional change models indicate that, with a lag of 1 year and controlling for a range of background characteristics, bicycle commuting and non-work cycling (in number of weekly trips) have a positive reciprocal influence on each other. The models show that work-related factors, such as the distance to work or whether a person receives a travel allowance, affect not only bicycle commuting but also non-work cycling. The latent transition model indicates that people can be clustered into four groups: non-cyclists, non-work cyclists, all-around cyclists and commuter cyclists. This model shows that people with a consistent propensity to not cycle at all (non-cyclists) or to cycle for both work and non-work purposes (all-around cyclists) are most stable in their travel behavior. Non-work cyclists and commuter cyclists are less stable in travel behavior. The model also shows that all-around cyclists are not (significantly) affected by a change in the distance to work. The article concludes with several directions for future research.     

Bicycling renaissance in North America?: Recent trends and alternative policies to promote bicycling

Over the past two decades, the number of bicycle trips in the United States has doubled. Since 48% of trips by all modes in American cities are shorter than three miles, the potential for further growth in bicycling seems enormous. So far, efforts to promote bicycling have focused on building bike paths and bike lanes. Although necessary, separate cycling facilities must be complemented by a comprehensive program to make all roads bikeable, through both physical adaptations and enforcement of cyclists' right to use the road. It seems likely that cycling will continue to grow in North America, but that its mode share will remain far lower than levels in northern Europe. Bicycling in Canada and especially the United States is impeded by the lack of a tradition of cycling for utilitarian purposes and by the marginal legal, cultural and infrastructure status of cyclists in both countries' automobile-based transport systems. As long as car use remains cheap and transportation policy remains dominated by motoring, bicycles will continue to be used primarily for recreation and not for daily urban travel in North America.