Applying interpretable machine learning to classify tree and utility pole related crash injury types

In spite of enormous improvements in vehicle safety, roadway design, and operations, there is still an excessive amount of traffic crashes resulting in injuries and major productivity losses. Despite the many studies on factors of crash frequency and injury severity, there is still further research to be conducted. Tree and utility pole/other pole related (TUOP) crashes present approximately 12 to 15% of all roadway departure (RwD) fatal crashes in the U.S. The count of TUOP crashes comprise nearly 22% of all fatal crashes in Louisiana. From 2010 to 2016, there were 55,857 TUOP crashes reported in Louisiana. Individually examining each of these crash reports is not a realistic option to investigate crash factors. Therefore, this study employed text mining and interpretable machine learning (IML) techniques to analyze all TUOP crashes (with available crash narratives) that occurred in Louisiana from 2010 to 2016. This study has two major goals: 1) to develop a framework for applying machine learning models to classify injury levels from unstructured textual content, and 2) to apply an IML framework that provides probability measures of keywords and their association with the injury classification. The present study employed three machine learning algorithms in the classification of injury levels based on the crash narrative data. Of the used modeling techniques, the eXtreme gradient boosting (XGBoost) model shows better performance, with accuracy ranging from 0.70 to 24% for the training data and from 0.30% to 16% for the test data.     

Modeling cyclists' facility choice and its application in bike lane usage forecasting

In some circumstances on streets equipped with new bike facilities, cyclists are not interested in using them. Instead, they continue to use shared spaces with pedestrians or motor vehicles. Thus, simply adding a bike facility does not guarantee that cyclists will switch to using it. Owing to the considerable development of bike facilities, the investigation of facility preference, particularly focusing on facility choice forecast, has become increasingly important. This study developed a model for predicting the facility choice of cyclists between on-street facilities (curb, traffic lane, and bike lane (BL)) and off-street facilities (sidewalks). Initially, the optimal model was selected using Bayesian Model Averaging method. Then, it was validated by both internal and external validations. Apart from the aforementioned factors, several other exogenous variables were also found to be significant predictors of bike facility choice, including the width of traffic lanes, existence of real-time stopping vehicle, type of bike, bus stop existence, and in-group cycling. Analysis of the relative importance of predictors indicated that bus stop existence, effective sidewalk width, and type of bike were the potential predictors. A framework for predicting BL usage, if it is present, was also developed. A test for the predictive performance of the application at a real site was carried out. By comparing predicted and actual BL usage figures, the analysis showed good predictive performance. The results of this study can help developers, planners, and designers to adopt reasonable investment decisions as well as better designs in developing new bike facilities.     

Effects of the autonomous vehicle crashes on public perception of the technology

In March 2018, an Uber-pedestrian crash and a Tesla's Model X crash attracted a lot of media attention because the vehicles were operating under self-driving and autopilot mode respectively at the time of the crash. This study aims to conduct before-and-after sentiment analysis to examine how these two fatal crashes have affected people's perceptions of self-driving and autonomous vehicle technology using Twitter data. Five different and relevant keywords were used to extract tweets. Over 1.7 million tweets were found within 15 days before and after the incidents with the specific keywords, which were eventually analyzed in this study. The results indicate that after the two incidents, the negative tweets on “self-driving/autonomous” technology increased by 32 percentage points (from 14% to 46%). The compound scores of “pedestrian crash”, “Uber”, and “Tesla” keywords saw a 6% decrease while “self-driving/autonomous” recorded the highest change with an 11% decrease. Before the Uber-incident, 19% of the tweets on Uber were negative and 27% were positive. With the Uber-pedestrian crash, these percentages have changed to 30% negative and 23% positive. Overall, the negativity in the tweets and the percentage of negative tweets on self-driving/autonomous technology have increased after their involvement in fatal crashes. Providing opportunities to interact with this developing technology has shown to positively influence peoples' perception.     

Inclusion of speed and weather measures in safety performance functions for rural roadways

The research on relationships among vehicle operating speed, roadway design elements, weather, and traffic volume on crash outcomes will greatly benefit the road safety profession in general. If these relationships are well understood and characterized, existing techniques and countermeasures for reducing crash frequencies and crash severities could potentially improve, and the opportunity for new methodologies addressing and anticipating crash occurrence would naturally ensue. This study examines the prevailing operating speeds on a large scale and determines how traffic speeds and different speed measures interact with roadway characteristics and weather condition to influence the likelihood of crashes. This study used three datasets from Washington and Ohio: 1) Highway Safety Information System (HSIS), 2) the National Performance Management Research Dataset (NPMRDS), and 3) National Oceanic and Atmospheric Administration (NOAA) weather data. State-based conflated databases were developed using the linear conflation of HSIS and NPMRDS. The results show that certain speed measures were found to be beneficial in quantifying safety risk. Annual-level crash prediction models show that increased variability in hourly operating speed within a day and an increase in monthly operating speeds within a year are both associated with a higher number of crashes. Safety practitioners can benefit from the current study in addressing the issue of speed and weather in crash outcomes.     

Normal and risky driving patterns identification in clear and rainy weather on freeway segments using vehicle kinematics trajectories and time series cluster analysis

Enhancing traffic safety on freeways is the main goal for all transportation agencies. However, to achieve this goal, many analysis protocols of network screening models need to be improved through considering human factors while analyzing traffic data. This paper introduces one on the new analysis protocol of identifying and discriminating between normal and risky driving in clear and rainy weather. The introduced analysis protocol will consider the effect of human factors on updating the networking screening process of identifying hotspots of crash risk. This paper employs the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) data to investigate the behavior of normal and risky driving under both rainy and clear weather conditions. Near-crash events on freeways, which were used as Surrogate Measure of Safety (SMoS) for crash risk, were identified based on the changes in vehicle kinematics, including speed, longitudinal and lateral acceleration and deceleration rates, and yaw rates. Through a trajectory-level data analysis, there were significant differences in driving patterns between rainy and clear weather conditions; factors that affected crash risk mainly included driver reaction and response time, their evasive maneuvers such as changes in acceleration rates and yaw rates, and lane-changing maneuvers. A cluster analysis method was employed to classify driving patterns into two clusters: normal and risky driving condition patterns, respectively. Statistical results showed that risky driving patterns started on average one second earlier in rainy weather conditions than in clear weather conditions. Furthermore, risky driving patterns extended in average three seconds in rainy weather conditions, while it was two seconds in clear weather conditions. The identification of these patterns is considered as a primary step towards an automated development that would distinguish between different driving patterns in a Connected Vehicle CV environment using Basic Safety Messages (BSM) and to enhance the network screening analysis for increased crash risk hotspots.     

Association of reduced visibility with crash outcomes

Most of the information necessary for driving a vehicle is regarded as visual information. In spite of its importance, visibility conditions at the time of a crash are often not documented at a high level of detail. Past studies have not examined the quantified values of visibility and its association with crashes. The current study merged data collected from the National Oceanic and Atmospheric Administration (NOAA) with 2010–2012 Florida crash data. From the thousands of logged weather events compiled by the NOAA, the researchers isolated periods of normal visibility and comparable periods of reduced visibility in a matched-pairs study. The NOAA data provided real visibility score based on the spatiotemporal data of the crashes. Additionally, the crash data, obtained from Roadway Information Database (RID), contains several geometric and traffic variables that allow for effects of factors and visibility. The study aims to associate crash occurrence under different levels of visibility with factors included in the crash database by developing ordinal logistic regression. The intent is to observe how different visibility conditions contribute to a crash occurrence. The findings indicate that the likelihood of a crash increase during periods of low visibility, despite the tendency for less traffic and for lower speeds to prevail during these times. The findings of this study will add valuable knowledge to the realm of the impact of visibility in the way of using and designing appropriate countermeasures.     

Severity analysis of tree and utility pole crashes: Applying fast and frugal heuristics

A roadway departure (RwD) crash is defined as a crash that occurs after a vehicle crosses an edge line or a center line, or otherwise leaves the designated travel path. RwD crashes account for approximately 50% of all traffic fatalities in the U.S. Additionally, crashes related to roadside fixed objects such as trees, utility poles, or other poles (TUOP) make up 12–15% of all fatal RwD crashes in the U.S. Data spanning over seven years (2010–2016) shows that TUOP crashes account for approximately 22% of all fatal crashes in Louisiana, which is significantly higher than the national statistic. This study aims to determine the effect of crash, geometric, environmental, and vehicle characteristics on TUOP crashes by applying the fast and frugal tree (FFT) heuristics algorithm to Louisiana crash data. FFT identifies five major cues or variable threshold attributes that contribute significantly to predicting TUOP crashes. These cues include posted speed limit, primary contributing factor, highway type, weather, and locality type. The balanced accuracy is around 56% for both training and test data. The current model shows higher accuracies compared to machine learning models (e.g., support vector machine, CART). The present findings emphasize the importance of a comprehensive understanding of factors that influence TUOP crashes. The insights from this study can help data-driven decision making at both planning and operation levels.     

Investigating fatal and injury crash patterns of teen drivers with unsupervised learning algorithms

Teenagers have been emphasized as a critical driver population class because of their overrepresentation in fatal and injury crashes. The conventional parametric approaches rest on few predefined assumptions, which might not always be valid considering the complicated nature of teen drivers' crash characteristics that are reflected by multidimensional crash datasets. Also, individual attributes may be more speculative when combined with other factors. This research employed joint correspondence analysis (JCA) and association rule mining (ARM) to investigate the fatal and injury crash patterns of at-fault teen drivers (aged 15 to 19 years) in Louisiana. The unsupervised learning algorithms can explore meaningful associations among crash categories without restricting the nature of variables. The analyses discover intriguing associations to understand the potential causes and effects of crashes. For example, alcohol impairment results in fatal crashes with passengers, daytimes severe collisions occur to unrestrained drivers who have exceeded the posted speed limits, and adverse weather conditions are associated with moderate injury crashes. The findings also reveal how the behavior patterns connected with teen driver crashes, such as distracted driving in the morning hours, alcohol intoxication or using cellphone in pickup trucks, and so on. The research results can lead to effectively targeted teen driver education programs to mitigate risky driving maneuvers. Also, prioritizing crash attributes of key interconnections can help to develop practical safety countermeasures. Strategy that covers multiple interventions could be more effective in curtailing teenagers' crash risk.     

Odds of work zone crash occurrence and getting involved in advance warning,transition, and activity areas by injury severity

Work zone area types include advance warning area, transition area, and activity area. The geometric conditions, traffic control aspects, traffic operations, and driver's maneuverability differ within each work zone area type. Therefore, the odds of getting involved in a crash and factors associated with injury severity vary by work zone area type. The focus of this research is to examine the odds of getting involved in a crash in work zone advance warning, transition, and activity areas by injury severity. Five years (2010–2014) of crash data for the state of North Carolina was obtained from the Highway Safety Information Systems (HSIS) and used in this research. Three partial proportional odds models and one proportional odds model were developed using Statistical Analysis Software (SAS) in this research. The results indicate that the odds of getting involved in a work zone crash in the transition area when compared to the advance warning area is higher during cloudy weather condition, on wet roads and interstates, and on roads equipped with double yellow / no passing zone, with rigid post barrier, grass, and flexible post barrier median. Further, the odds of getting involved in a work zone crash in the activity area when compared to the advance warning area is higher during cloudy weather condition, on interstate and US routes, and on roads with stop and go signal, double yellow / no passing zone and flexible post barrier median. Overall, the findings indicate that the odds and factors associated with crash occurrence depend on the work zone area type. The odds of getting involved in a severe or moderate injury crash is higher on curved roads in all the three work zone area types compared to straight roads. It is higher 1) in the advance warning area on roads with semi-flexible post barrier medians, 2) in the transition area on US routes, and 3) in the activity area on dark lighted roads, US routes, and State routes. Overall, the odds of getting involved in a severe or moderate injury crash and associated factors vary by work zone area type. The findings from this research assist the practitioners to take precautionary measures and reduce the odds of getting involved in a crash by implementing work zone area-specific safety countermeasures.     

Modeling lane-change-related crashes with lane-specific real-time traffic and weather data

This study investigates the relationship between lane-change-related crashes and lane-specific, real-time traffic factors. It is anticipated that the real-time traffic data for the two lanes—the vehicle's lane (subject lane) and the lane to which that a vehicle intends to change (target lane)—are more closely related to lane-change-related crashes, as opposed to congregated traffic data for all lanes. Lane-change-related crash data were obtained from a 62-mile long freeway in Southeast Wisconsin in 2012 and 2013. One-minute traffic data from the 5- to 10-minute interval prior to the crashes were extracted from an immediately upstream detector station and two immediately downstream stations from the crash location. Weather information was collected from a major historical weather database. A matched case-control logistic regression was used for analysis. Results show that the following factors significantly affect the probability of a lane-change-related crash: average flow into the target lane at the first downstream station, the flow ratio at the second downstream station, and snow conditions. Additionally, the average speed in the target lane at the first downstream station contributes to the occurrence of lane-change crashes during snowy conditions. According to the model, the probability of a lane-change-related crash under real-time traffic conditions can aid in flagging potential crash-prone conditions. The identified contributing factors can help traffic operators select traffic control and management countermeasures to proactively mitigate lane-change-related crashes.     

Modeling the accuracy of traffic crash prediction models

Crash forecasting enables safety planners to take appropriate actions before casualty or loss occurs. Identifying and analyzing the attributes influencing forecasting accuracy is of great importance in road crash forecasting. This study aims to model the forecasting accuracy of 31 provinces using their macroeconomic variables and road traffic indicators. Iran's road crashes throughout 2011–2018 are calibrated and cross-validated using the Holt-Winters (HW) forecasting method. The sensitivity of crash forecast reliability is studied by a regression model. The results suggested that the root mean square error (RMSE) of crash prediction increased among the provinces with higher and more variant average monthly crashes. On the contrary, the accuracy of crash prediction improved in provinces with higher per capita GDP, and higher traffic exposure. A 1% increase in crash variability, average historical crash count, GDP per capita, and traffic exposure, respectively, resulted in a 0.65%, 0.52%, −0.38%, and −0.13% change in the RMSE of forecasting. The addition of traffic exposure and macroeconomic factors significantly enhanced the model fit and improved the adjusted R-squared by 14% compared to the reduced model that only used the historical average and variability of crash count as the independent variables. The findings of this research suggest planners and policymakers should consider the notable influence of macroeconomic factors and traffic indicators on the crash forecasting accuracy.     

基于时空交互模型的高速公路季节事故频次影响因素分析

为了深入了解影响高速公路事故频次的显著因素,采集2014年广东省开阳高速公路的事故、道路、交通和气象数据,以曲率和坡度同质性为原则将整条公路划分为154条路段,采用时空交互模型拟合路段季节事故数和道路设计参数、交通特征、气象因素间的内在关系。该模型不仅解释了相邻路段间的空间效应和相邻季节间的时间效应,而且还考虑了时空效应间的相互作用,有助于提高模型的拟合预测性能、减少参数估计偏倚。基于贝叶斯推断的模型估计和评价结果显示：事故数据中存在显著的时空关联和交互效应;时空交互模型比传统层级泊松模型的拟合优度更高;路段长度与事故频次线性相关,而交通量则与事故频次间存在非线性关系;高速公路交通安全性随着中、大型客、货车（三类车）比例的增加而显著提高;路段曲率、坡度越大,交通事故风险越高;风速越高、降水量越多的季节,事故频次将显著上升。研究结果可为高速公路交通安全改善方案的制定提供理论依据。     

SUV车型外观评论文本情感分析

为了解决SUV车型外观海量评论文本隐藏信息挖掘分析问题,给出了一个基于LDA主题模型的数据挖掘方法.通过LDA主题模型识别出评论文本中潜藏的主题信息,计算感兴趣的文本主题和文本涵盖的主题比例.经过情感信息抽取、情感信息分类和情感分析建模等步骤,实现对文本评论的倾向性判断和隐藏信息挖掘,得到SUV车型的用户情感倾向分析结...     

Using on-board video data for safety analysis – An analysis of right hook crashes involving large buses at signalized intersections

Bus right hook (BRH) crashes at intersections are one of the most common types of crashes for bus carriers, which accounted for as high as 16% of fatal and injury crashes involving large buses at intersections in Taiwan. A BRH crash occurs when a bus and another vehicle traveling in the same direction head into an intersection, but the bus driver makes a right turn across the path of the through-moving vehicle, and both vehicles collide. This study responds to the research needs to identity factors associated with BRH crashes by utilizing in-vehicle data recorder (IVDR) data. A four step analysis procedure was developed, including (1) video data coding, (2) crash sequence analysis to identify crash contributing factors, (3) a case-control study to examine the relationship between the crash contributing factors and crash occurrence, and (4) modeling crash risk in terms of the crash contributing factors to better understand the crash generating process. This study first identified the existence of driver unattended time as the time between when the driver last checked the right back mirror to finally steering for a right turn, indicating the time period wherein the driver did not track the through vehicle on the right side using the right back mirror. It was found that BRH crashes could be attributed to the concurrence of unattended time and the speed difference between the bus and through vehicle. Several recommendations are discussed based on the results to further develop countermeasures to reduce this type of crash.     

共享电单车在山地城市道路的适应性研究

共享电单车作为“最后一公里”较好的出行选择，因其具有良好的驱动性和便捷性，在道路坡度较大的山地城市拥有较高的潜在出行需求。然而，作为一种新兴出行模式，共享电单车是否适应山地城市，山地城市道路如何适应并促进共享电单车的发展推广，目前尚无相关的研究。为填补该领域研究空白，提升共享电单车用户群体的出行安全，促进共享电单车在山地城市的推广，优化共享电单车在山地城市的规划布设，通过问卷调查、实地试验以及运营大数据分析等方法，分析了共享电单车用户的骑行体验、骑行偏好、道路爬坡能力、出行分布等信息，认为共享电单车用户的人行道骑行体验普遍劣于车行道骑行体验，在道路规划设计时，应结合实地道路路权使用情况，尽可能在车行道设置安全的非机动车道；共享电单车可以在坡度不超过12%的山地城市道路完成爬坡，但坡度超过11%时，电单车的骑行速度受到较大影响，建议设置非机动车道的道路最大纵坡控制在11%以内；电单车用户群体主要根据出行目的选择路线，且更愿意在宽阔的主、次干路骑行，建议在坡度小于11%的主、次干路设置电单车通勤路线；共享电单车需根据山地城市道路特点，增加及提升车辆本身的设备，如增加反光镜、夜间尾灯亮度等。     

Modeling injury severity of crashes involving trucks: Capturing and exploring risk factors associated with land use and demographic in addition to crash,driver, and on-network characteristics

Nearly 499,000 motor vehicle crashes involving trucks were reported across the United States in 2018, out of which 22% resulted in fatalities and injuries. Given the growing economy and demand for trucking in the future, it is crucial to identify the risk factors to understand where and why the likelihood of getting involved in a severe or moderate injury crash with a truck is higher. The focus of this research, therefore, is on developing a methodology, capturing and integrating data, exploring, and identifying risk factors associated with surrounding land use and demographic characteristics in addition to crash, driver, and on-network characteristics by modeling injury severity of crashes involving trucks. Crash data for Mecklenburg County in North Carolina from 2013 to 2017 was used to develop partial proportional odds model and identify risk factors influencing injury severity of crashes involving trucks. The findings indicate that dark lighting condition, inclement weather condition, the presence of double yellow or no-passing zone, road sections with speed limit >40 mph and curves, and driver fatigue, impairment, and inattention have a significant influence on injury severity of crashes involving trucks. These outcomes indicate the need for effective geometric design and improved visibility to reduce the injury severity of crashes involving trucks. The likelihood of a severe or moderate injury crash involving a truck is also high in areas with high employment, government, light commercial, and light industrial land uses. The findings can be used to identify potential risk areas, proactively plan and prioritize the allocation of resources to improve safety of transportation system users in these areas.     

Macroscopic analysis on the frequency and severity of pedestrian crashes on National Roads in Metro Manila,Philippines

Pedestrians are the most vulnerable road users; thus, understanding the primary factors that lead to pedestrian crashes is a chief concern in road safety. However, owing to the limitations of crash data in developing countries, only a few studies have evaluated the comprehensive characteristics of pedestrian crashes, specifically on different road types. This study attempted to develop pedestrian crash frequency and severity models on national roads by using the road characteristics and built environment parameters, based on the road crash data (2016–2018) that involved pedestrians in Metro Manila, Philippines. Remarkable findings included primary roads, presence of footbridges, road sections with bad surface conditions, and increased fractions of commercial, residential, and industrial roads, which exhibited a greater likelihood of pedestrian crashes. Crashes involving elderly pedestrians, heavier vehicles, late-night hours, fair surface conditions, and open spaces were associated with increased likelihoods of fatal outcomes. Essentially, this study provides a macroscopic perspective in understanding the factors associated with the severity and frequency of pedestrian crashes, and it would aid the authorities in identifying proper countermeasures.     

Modeling phone use prevalence and risk assessment among long-haul truck drivers in India

Research on distracted driving due to phone use has increased substantially over the past decades, however, very little is explored about commercial vehicle drivers (e.g., truck drivers) in this aspect. This study focused on examining the prevalence of phone use habits and the associated crash risk for data collected from 490 Indian truck drivers. The data on demographic details, driving history, phone use habits (in everyday life and during driving), history of receiving any penalty for phone use and incidences of crash occurrence, was collected through face-to-face interviews with the drivers. Binary logistic models were used to identify the factors affecting phone use habits during driving and the associated crash risk. Further, the incidences of receiving a penalty for the phone use were examined through cross-tabulation and chi-square statistics. The results showed that 55% of the drivers used a phone during driving, mainly for talking purpose. The model revealed that education, vehicle size, vehicle ownership and everyday life phone use habits were the significant factors associated with phone use while driving. Regarding the history of penalty receiving incidences, 41% of the drivers who used a phone during driving had received the penalty, and 52% of these penalty-receiving drivers were penalized repetitively. The model results for crash risk showed that the frequent phone users were 29 times more likely to be involved in a crash due to phone use compared to the non-frequent users. The results suggest a double level (legislative and company level) prohibition policy for phone use during driving for the truck drivers and also to enforce strict and effective legislative ban especially on the truck routes.     

Factors influencing crash involvement during early years of owning a new vehicle

Though automobile manufacturers are investing efforts to make newer vehicles safer to drive, an element of uncertainty with the new vehicle seems to persist with the drivers during the early years of ownership. This could be due to a lack of familiarity of the vehicle's power, dimensions or available technologies/features. While the uncertainty in itself is a potential cause of a crash, it is important for the policy-makers, practitioners, and automobile manufacturers to understand the factors that could further aggravate the problem. This research focuses on identifying the factors influencing the likelihood of getting involved in a crash and its severity when driving a new vehicle. Crash data for North Carolina for the years 2013 to 2018 (six years) was used develop partial proportionality odds models, compute the odds ratios, analyze the effects of explanatory variables, and identify factors influencing crashes by the age of the vehicle. The likelihood of getting involved in a severe or moderate injury crash when driving a new vehicle is less for drivers in the age group ≤19 years. Erratic driving behavior (like making wide turns, weaving and swerving in traffic, driving with headlights off, driving on center-line or lane-line, etc.) and speeding increase the risk of getting involved in a moderate injury crash when driving a new vehicle. Likewise, the odds of getting involved in a crash are high on weekends and in adverse weather conditions when driving a new vehicle. They are higher when driving a new motorcycle, heavy vehicle or farm machinery. The findings help policy-makers and practitioners formulate strategies to educate drivers on factors influencing crash risk when driving a new vehicle. Further, automobile manufacturers can establish guidance programs and documentation that explain what to expect when buying and driving a new vehicle.     

Cycling in sub-Saharan African cities: Differences and similarities with developed world cities

Cycling is a vital transport mode for many of the Sub-Saharan African (SSA) cities given the limited transport options that exist. Despite its enormous commuting importance in SSA cities, little scientific research has attempted to identify key factors influencing cycling adoption, and most existing cycling promotional initiatives are often not contextualised to the African cities. To underpin appropriate incentives to promote bicycle commuting, this study conducts a literature review to identify key determinants of bicycle use in SSA cities. Moreover, it identifies key differences and similarities with cycling studies from the developed world cities (DWC). A survey of relevant literature was conducted through the Web of Science, Scopus, PubMed and Google scholar. This allowed gathering 61 relevant empirical study materials that helped to identify main factors influencing cycling in both SSA and DWC urban contexts, based on the socio-economic, built-environment, weather conditions and environmental and attitudinal factors. The results found that the vast list of factors influencing cycling, such as gender, education level, income, street signage, road encroachment, weather change, travel distance, the opportunity for flexible jobs and image prestige present a deep difference between studies in the two urban contexts. Street lighting, rain and tree cover present more consensual understanding among researchers in both urban contexts. This study reinforces that knowledge on cycling and its promotional initiatives should not be generalized, but rather be focused on the contextual setting of a particular city. In review of the past studies the limitation observed is that some specific characteristics of cycling in SSA cities such as the use of bicycle for commercial purpose is not covered in most cycling literature from the DWC. Given the observed contextual differences between cities from SSA and DWC, the study suggests the need for further research in quantifying and comparing the strength of the similarities and differences in cycling behaviour influences.