Person: Lusk, Anne
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Lusk
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Lusk, Anne
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Publication Biking practices and preferences in a lower income, primarily minority neighborhood: Learning what residents want(Elsevier, 2017) Lusk, Anne; Anastasio, Albert; Shaffer, Nicholas; Wu, Juan; Li, YanpingThis paper examines if, in a lower-income minority neighborhood, bicycling practices and bicycle-environment preferences of Blacks and Hispanics were different from Whites. During the summer of 2014, surveys were mailed to 1537 households near a proposed cycle track on Malcolm X Boulevard in Roxbury, MA. On the Boulevard, intercept surveys were distributed to cyclists and observations noted about passing cyclist's characteristics. Data were analyzed from 252 returned-mailed surveys, 120 intercept surveys, and 709 bicyclists. White (100%), Hispanic (79%), and Black (76%) bicyclists shown pictures of 6 bicycle facility types in intercept surveys perceived the cycle track as safest. More White mailed-survey respondents thought bikes would not be stolen which may explain why more Hispanics (52%) and Blacks (47%) preferred to park their bikes inside their home compared with Whites (28%), with H/W B/W differences statistically significant (p < 0.05). More Hispanic (81%) and Black (54%) mailed-survey respondents thought they would bicycle more if they could bicycle with family and friends compared with Whites (40%). Bicyclists observed commuting morning and evening included Blacks (55%), Whites (36%) and Hispanics (9%). More Whites (68%) wore helmets compared with Hispanics (21%) and Blacks (17%) (p < 0.001). More Blacks (94%) and Hispanics (94%) rode a mountain bike compared with Whites (75%). Minority populations are biking on roads but prefer cycle tracks. They also prefer to park bikes inside their homes and bicycle with family and friends. Wide cycle tracks (bicycling with family/friends) and home bike parking should be targeted as capital investments in lower-income minority neighborhoods.Publication Cycle Tracks and Parking Environments in China: Learning from College Students at Peking University(MDPI, 2017) Yuan, Changzheng; Sun, Yangbo; Lv, Jun; Lusk, AnneChina has a historic system of wide cycle tracks, many of which are now encroached by cars, buses and bus stops. Even with these conditions, college students still bicycle. On campuses, students park their bikes on facilities ranging from kick-stand-plazas to caged sheds with racks, pumps and an attendant. In other countries, including Canada, some of the newer cycle tracks need to be wider to accommodate an increasing number of bicyclists. Other countries will also need to improve their bike parking, which includes garage-basement cages and two-tiered racks. China could provide lessons about cycle tracks and bike parking. This study applied the Maslow Transportation Level of Service (LOS) theory, i.e., for cycle tracks and bike parking, only after the basic needs of safety and security are met for both vehicle occupants and bicyclists can the higher needs of convenience and comfort be met. With random clustering, a self-administered questionnaire was collected from 410 students in six dormitory buildings at Peking University in Beijing and an environmental scan of bicycle parking conducted in school/office and living areas. Cycle tracks (1 = very safe/5 = very unsafe) shared with moving cars were most unsafe (mean = 4.6), followed by sharing with parked cars (4.1) or bus stop users (4.1) (p < 0.001). Close to half thought campus bike parking lacked order. The most suggested parking facilities were sheds, security (guard or camera), bicycle racks and bicycle parking services (pumps, etc.). If parking were improved, three quarters indicated they would bicycle more. While caged sheds were preferred, in living areas with 1597 parked bikes, caged sheds were only 74.4% occupied. For the future of China’s wide cycle tracks, perhaps a fence-separated bus lane beside a cycle track might be considered or, with China’s recent increase in bike riding, shared bikes and E-bikes, perhaps cars/buses could be banned from the wide cycle tracks. In other countries, a widened cycle track entrance should deter cars. Everywhere, bike parking sheds could be built and redesigned with painted lines to offer more space and order, similar to car parking.Publication Database improvements for motor vehicle/bicycle crash analysis(BMJ Publishing Group, 2015) Lusk, Anne; Asgarzadeh, Morteza; Farvid, MaryamBackground: Bicycling is healthy but needs to be safer for more to bike. Police crash templates are designed for reporting crashes between motor vehicles, but not between vehicles/bicycles. If written/drawn bicycle-crash-scene details exist, these are not entered into spreadsheets. Objective: To assess which bicycle-crash-scene data might be added to spreadsheets for analysis. Methods: Police crash templates from 50 states were analysed. Reports for 3350 motor vehicle/bicycle crashes (2011) were obtained for the New York City area and 300 cases selected (with drawings and on roads with sharrows, bike lanes, cycle tracks and no bike provisions). Crashes were redrawn and new bicycle-crash-scene details were coded and entered into the existing spreadsheet. The association between severity of injuries and bicycle-crash-scene codes was evaluated using multiple logistic regression. Results: Police templates only consistently include pedal-cyclist and helmet. Bicycle-crash-scene coded variables for templates could include: 4 bicycle environments, 18 vehicle impact-points (opened-doors and mirrors), 4 bicycle impact-points, motor vehicle/bicycle crash patterns, in/out of the bicycle environment and bike/relevant motor vehicle categories. A test of including these variables suggested that, with bicyclists who had minor injuries as the control group, bicyclists on roads with bike lanes riding outside the lane had lower likelihood of severe injuries (OR, 0.40, 95% CI 0.16 to 0.98) compared with bicyclists riding on roads without bicycle facilities. Conclusions: Police templates should include additional bicycle-crash-scene codes for entry into spreadsheets. Crash analysis, including with big data, could then be conducted on bicycle environments, motor vehicle potential impact points/doors/mirrors, bicycle potential impact points, motor vehicle characteristics, location and injury.