J Community Health DOI 10.1007/s10900-015-0001-9
ORIGINAL PAPER
Pedestrian Behavior at Five Dangerous and Busy Manhattan Intersections Corey H. Basch • Danna Ethan • Patricia Zybert Charles E. Basch
•
Ó Springer Science+Business Media New York 2015
Abstract Technology-related distracted behavior is an emergent national concern. Listening to, looking at or talking into an electronic device while walking divides attention, increasing the risk of injury. The purpose of this study was to quantify technology-related distracted pedestrian behavior at five dangerous and busy Manhattan intersections. Data were collected over ten cycles of signal changes at each of the four corners of five intersections at four times of day. Data for ‘Walk’ and ‘Don’t Walk’ signals were tallied separately. A total of 21,760 pedestrians were observed. Nearly one-third crossing on a ‘Walk’ signal (n = 5414, 27.8 %), and nearly half crossing on a ‘Don’t Walk’ signal (n = 974; 42.0 %) were wearing headphones, talking on a mobile phone, and/or looking down at an electronic device. Headphone use was the most common distraction. Keywords Manhattan
Technology Distracted Pedestrians
Introduction Technology-related distracted behavior is an emergent national concern [1]. Based on a national review of hospital emergency room records, injuries related to mobile phone use in pedestrians increased tenfold from .37 % of pedestrian injuries in 2005 to 3.67 % in 2010 [2]. One national study concluded that, of headphone-related pedestrian-vehicle crashes, 29 % involved failure to respond to an audible warning [3]. In New York City (NYC), it has been estimated that pedestrian choices may cause *30 % of pedestrian fatalities [5]; 168 pedestrians were killed in NYC by cars in 2013 [4]. Just as using technology while driving is dangerous, listening to, looking at or talking into an electronic device while walking divides attention and increases the risk of injury [6–11], yet little is known about the frequency of these behaviors. The purpose of this study was to quantify technology-related distracted pedestrian behavior at five dangerous and busy intersections in Manhattan.
Methods C. H. Basch (&) Department of Public Health, William Paterson University, Wing 143, Wayne, NJ 07470, USA e-mail: [email protected] D. Ethan Health Education and Promotion Program, Lehman College, The City University of New York, Bronx, NY, USA P. Zybert Teachers College, Columbia University, New York, NY, USA C. E. Basch Health Education at Teachers College, Columbia University, New York, NY, USA
Pilot study data on distracted walking [12] was used to select the five highest density Manhattan intersections among those deemed most dangerous [13]. Borrowing methods of the pilot study [12], one coder tallied instances of the following pedestrian behaviors coming towards them: talking on a mobile phone, wearing headphones, looking down at a mobile device, engaging in a combination of these behaviors. Another coder tallied total pedestrian flow. At each intersection, data were collected at four times of the day: morning commute (7:30–9:30 a.m.), morning recreation (9:31 a.m.–12:29 p.m.), afternoon
123
J Community Health
recreation (12:30–4:29 p.m.), and evening commute (4:30–6:30 p.m.). Ten cycles of signal changes (i.e., ‘Walk’ to ‘Don’t Walk’) were observed at each of the four corners of each of the five intersections. At each intersection, ten signal cycles was equivalent to 15 min. Data for ‘Walk’ and ‘Don’t Walk’ signals were tallied separately, the latter including only pedestrians who initiated crossing when the signal was solid red. The unit of analysis was chosen to be the time-specific intersection (i.e.; n = 20; 5 intersections 9 4 times of day). The matched pair Wilcoxon signed rank test was used to determine if there were differences in the rates of distracted walking behavior during the ‘Walk’ and ‘Don’t Walk’ signals for each behavior separately (i.e., wearing headphones, looking down at device, and talking on a mobile phone) as well as for any distracted behavior. To assess inter-rater reliability, both coders simultaneously tallied either total pedestrian flow or distracted behaviors at each of the five intersections during ten ‘Walk’ signals. The two tallies were discrepant an average of under .5 % of total pedestrian flow per intersection. The Institutional Review Boards at William Paterson University, Lehman College, City University of New York, and Teachers College, Columbia University approved this study.
Table 1 Pedestrian volume at five Manhattan intersections during 40 light signal cycles, 10 per corner, by time of day Location 34th St and 6th Ave
34th St and 7th Ave
34th St and 8th Ave
42nd St and 6th Ave
42nd St and 8th Ave
Time of day
Number of Pedestrians
a.m. commute
725
a.m. recreation
852
p.m. recreation p.m. commute
878 1131
a.m. commute
1731
a.m. recreation
1459
p.m. recreation
1418
p.m. commute
1847
a.m. commute
958
a.m. recreation
684
p.m. recreation
722
p.m. commute
1189
a.m. commute
1321
a.m. recreation
531
p.m. recreation
579
p.m. commute
1083
a.m. commute
1163
a.m. recreation p.m. recreation
654 1049
p.m. commute
1786
Results The total number of pedestrians observed at each of the five intersections and four times of day are presented in Table 1. Each cell represents data pooled across 10 signal cycles (i.e. 15 min) and four corners (i.e. total time 1 h). A total of 21,760 pedestrians were observed: 5898 during a.m. commute, 4180 during a.m. recreation, 4646 during p.m. recreation, and 7036 during p.m. commute. The data are categorized by ‘Walk’ and ‘Don’t Walk’ signals in Table 2. In all 20 observation periods, any distracted behavior rates were higher during the ‘Don’t Walk’ signal than during the ‘Walk’ signal. With one exception, headphone use rates were higher during the ‘Don’t Walk’ signal then during the ‘Walk’ signal. Nearly one-third of those crossing on a ‘Walk’ signal were distracted (n = 5414, 27.8 %), with wearing headphones the most common distraction (n = 3119, 16.0 %) followed by looking down at a device (n = 1012; 5.2 %) and talking on a mobile phone (n = 793; 4.1 %). Nearly one-half of those crossing on a ‘Don’t Walk’ signal were distracted (n = 974; 42.0 %), with wearing headphones the most common distraction (n = 564; 24.3 %), followed by looking down (n = 197; 8.5 %) and talking (n = 125; 5.4 %). Rates of multiple distracted behaviors were 2.5 % during a ‘Walk’ signal and 3.8 % during a ‘Don’t Walk’ signal. Matched pair signed rank tests confirm that each
123
distracted walking behavior and any distracted walking behavior was more frequent during the ‘Don’t Walk’ signal than during the ‘Walk’ signal.
Discussion A significant number of pedestrians are injured and killed in traffic crashes locally and nationally. In the United States in 2010, 4280 pedestrians were killed and another 70,000 injured in traffic crashes [14] Over 70 % of the fatalities were in urban areas [14]. This study is the largest to date describing technologybased distracted walking behaviors in NYC. Nearly onethird of pedestrians crossing on the ‘Walk’ and almost onehalf crossing on the ‘Don’t Walk’ signal at five busy Manhattan intersections were engaged with an electronic device. The most prevalent distracted behavior was headphone use, a behavior that impairs the ability to register important audible warnings [3]. The pilot study on which this study was based was the first study of distracted walking in NYC. The rate of distracted walking during the ‘Walk’ signal was nearly the same in the pilot and current studies (28.8 vs. 27.8 %). The rate of distracted walking during the ‘Don’t Walk’ signal was higher in the current study than in the pilot (42 vs.
231 (24.5) 395 (25.2)
p.m. recreation
p.m. commute 3.92 p = .000
360 (33.5) 147 (25.6)
a.m. commute
125 (23.7) 245 (25.2)
p.m. recreation p.m. commute
a.m. recreation
115 (24.3)
a.m. recreation
316 (28.7)
p.m. commute 398 (32.2)
187 (29.3)
p.m. recreation
a.m. commute
144 (23.5)
a.m. recreation
414 (25.7)
p.m. commute 286 (32.5)
269 (21.9)
p.m. recreation
a.m. commute
427 (32.6)
90 (40.7)
50 (47.6)
37 (46.3)
44 (49.4)
26 (50.0) 43 (39.4)
28 (48.3)
39 (45.3)
31 (35.6)
42 (50.6)
36 (50.7)
36 (46.2)
81 (34.5)
71 (37.0)
55 (36.9)
77 (51.7)
43 (36.1)
49 (51.0)
58 (33.1)
38 (45.2)
3.81 p = .000
222 (14.2)
110 (11.7)
59 (10.3)
267 (24.9)
50 (9.5) 112 (11.5)
43 (9.1)
281 (22.8)
168 (15.2)
91 (14.2)
62 (10.1)
215 (24.4)
242 (15.0)
128 (10.4)
260 (19.8)
407 (25.7)
98 (9.7)
86 (11.0)
73 (10.8)
145 (22.6)
26 (31.0)
44 (19.9)
27 (25.7)
9 (11.3)
34 (38.2)
12 (23.1) 26 (23.9)
23 (39.7)
17 (19.8)
14 (16.1)
18 (21.7)
22 (31.0)
29 (37.2)
53 (22.6)
37 (19.3)
36 (24.2)
62 (41.6)
24 (20.2)
18 (18.8)
33 (18.9)
* Standardized test statistic, matched pair Wilcoxon signed rank test, Walk versus Don’t Walk (n = 20); 2-tailed
Signed rank test*
42nd St and 8th Ave
42nd St and 6th Ave
34th St and 8th Ave
550 (34.8)
230 (22.7)
p.m. commute
a.m. recreation
206 (26.3)
p.m. recreation
a.m. commute
159 (23.5)
a.m. recreation
34th St and 7th Ave
210 (32.8)
a.m. commute
34th St and 6th Ave
Don’t Walk n (%)
Walk n (%)
Walk n (%)
Don’t Walk n (%)
Wearing headphones
Any distracted behavior
Time of day
Location
3.14 p = .002
63 (4.0)
53 (5.6)
32 (5.6)
39 (3.6)
30 (5.7) 57 (5.9)
36 (7.6)
40 (3.2)
63 (5.7)
40 (6.3)
45 (7.3)
29 (3.3)
66 (4.1)
74 (6.0)
70 (5.3)
66 (4.2)
67 (6.6)
64 (8.2)
49 (7.2)
29 (4.5)
Walk n (%)
18 (8.1)
11 (10.5)
13 (16.3)
6 (6.7)
2 (3.8) 4 (3.7)
3 (5.2)
5 (5.8)
11 (12.6)
11 (13.3)
7 (9.9)
2 (2.6)
19 (8.1)
15 (7.8)
14 (9.4)
8 (5.4)
11 (9.2)
18 (18.8)
12 (6.9)
7 (8.3)
Don’t Walk n (%)
Looking down at device
Table 2 Technology-related distracted pedestrian behavior at five Manhattan intersections by time of day and Walk and don’t Walk signal
2.17 p = .030
71 (4.5)
52 (5.5)
25 (4.4)
26 (2.4)
33 (6.3) 48 (4.9)
28 (5.9)
35 (2.8)
57 (5.2)
32 (5.0)
25 (4.1)
19 (2.2)
80 (5.0)
49 (4.0)
56 (4.3)
34 (2.1)
46 (4.5)
40 (5.1)
24 (3.5)
13 (2.0)
Walk n (%)
17 (7.7)
7 (6.7)
15 (18.8)
3 (3.4)
7 (13.5) 9 (8.3)
2 (3.4)
7 (8.1)
5 (5.7)
5 (6.0)
6 (8.5)
3 (3.8)
3 (1.3)
8 (4.2)
3 (2.0)
2 (1.3)
6 (5.0)
9 (9.4)
5 (2.9)
3 (3.6)
Don’t Walk n (%)
Talking on mobile phone
J Community Health
123
J Community Health
26.3 %) [12]. The intersections in the current study were selected because they were the busiest among the most dangerous in Manhattan. All were in midtown. In a study in Seattle, with 1102 pedestrians observed at 20 intersections, the rate of distracted walking was nearly 30 %, and headphone use was the most commonly observed distraction [15]. In a study in San Francisco, rates of technology related distracted walking varied by intersection, but peaked at 18 % [16]. This study is limited by its cross-sectional design and by its focus on only five intersections. We did not collect data that would have allowed us to assess differences between pedestrians walking with or against traffic (a relevant concept, especially during rush hour in midtown Manhattan). Nevertheless, with data at each of four time periods at each of the five busiest and most dangerous Manhattan intersections, this is the largest study of pedestrian distracted walking behavior conducted to date. Cheever and colleagues suggest that mobile device overuse may be a form of psychological dependency [17] and psychological research is exploring ways to dampen the desire to constantly ‘‘check in’’ (and enhance the sense of alertness to one’s surroundings) [18]. Future research should focus on means to increase pedestrian cooperation in remaining alert to their surroundings when crossing busy intersections. It is recommended that initiatives be implemented to heighten public awareness of the risks of distracted walking.
References 1. Smith, D., Schreiber, K., Saltos, A., et al. (2013). Ambulatory cell phone injuries in the United States: An emerging national concern. Journal Safety Research, 47, 19–23. 2. Nasar, J., & Troyer, D. (2013). Pedestrian injuries due to mobile phone use in public places. Accident Analysis and Prevention, 57, 91–95. 3. Lichenstein, R., Smith, D., Ambrose, J., & Moody, L. (2012). Headphone use and pedestrian injury and death in the United States: 2004–2011. Injury Prevention, 18, 287–290.
123
4. NYPD: 16,059 pedestrians and cyclists injured, 178 killed in traffic in 2013. Street Blog WebSite. http://www.streetsblog.org/ 2014/01/31/nypd-16059-pedestrians-and-cyclists-injured-178-kil led-in-traffic-in-2013/. Accessed September 14, 2014. 5. Vision Zero Action Plan 2014. Vision Zero Web Site. http:// www.nyc.gov/html/visionzero/pdf/nyc-vision-zero-action-plan. pdf. Accessed August 28, 2014. 6. Stavrinos, D., Byington, K., & Schwebel, D. (2009). Effect of cell phone distraction on pediatric pedestrian injury risk. Pediatrics, 123, e179–e185. 7. Schwebel, D., Stavrinos, D., Byington, K., et al. (2012). Distraction and pedestrian safety: How talking on the phone, texting, and listening to music impact crossing the street. Accident Analysis and Prevention, 45, 266–271. 8. Hatfield, J., & Murphy, S. (2007). The effects of mobile phone use on pedestrian crossing behaviour at signalised and unsignalised intersections. Accident Analysis and Prevention, 39, 197–205. 9. Bungum, T., Day, C., & Henry, L. (2005). The association of distraction and caution displayed by pedestrians at a lighted crosswalk. Journal of Community Health, 30, 269–279. 10. Strayer, D., & Drews, F. (2007). Cell-phone–induced driver distraction. Current Directions in Psychological Science, 16, 128–131. 11. Hosking, S., Young, K., & Regan, M. (2009). The effects of text messaging on young drivers. Human Factors, 51, 582–592. 12. Basch, C., Ethan, D., Rajan, S., & Basch, C. (2014). Technologyrelated distracted walking behaviors in Manhattan’s most dangerous intersections. Injury Prevention, 20, 343–346. 13. Top Ten Intersections by Crash and Borough. Crashstat Web Site. http://crashstat.org/sites/default/files/dangerous/Top%20Ten% 20Intersections%20by%20Crash%20Type%20and%20Boro. pdf. Accessed August 28, 2014. 14. Department of Transportation (US), National Highway Traffic Safety Administration (NHTSA). Traffic Safety Facts 2010: Pedestrians. Washington (DC). http://www-nrd.nhtsa.dot.gov/ Pubs/811625.PDF. Accessed August 28, 2014. 15. Thompson, L., Rivara, F., Ayyagari, R., et al. (2013). Impact of social and technological distraction on pedestrian crossing behavior: An observational study. Injury Prevention, 4, 232–237. 16. Cooper, J. F., Schneider, R. J., Ryan, S., et al. (2012). Documenting targeted behaviors associated with pedestrian safety. Transportation Research Record, 2299, 1. 17. Cheever, N., Rosen, L., Carrier, L., & Chavez, A. (2014). Out of sight is not out of mind: The impact of weapons of mass distraction on anxiety. Computers in Human Behavior, 37, 290–297. 18. Rosen, L. (2012). iDisorder: Understanding our obsession with technology and overcoming its hold on us. New York, NY: Palgrave Macmillan.
ORIGINAL PAPER
Pedestrian Behavior at Five Dangerous and Busy Manhattan Intersections Corey H. Basch • Danna Ethan • Patricia Zybert Charles E. Basch
•
Ó Springer Science+Business Media New York 2015
Abstract Technology-related distracted behavior is an emergent national concern. Listening to, looking at or talking into an electronic device while walking divides attention, increasing the risk of injury. The purpose of this study was to quantify technology-related distracted pedestrian behavior at five dangerous and busy Manhattan intersections. Data were collected over ten cycles of signal changes at each of the four corners of five intersections at four times of day. Data for ‘Walk’ and ‘Don’t Walk’ signals were tallied separately. A total of 21,760 pedestrians were observed. Nearly one-third crossing on a ‘Walk’ signal (n = 5414, 27.8 %), and nearly half crossing on a ‘Don’t Walk’ signal (n = 974; 42.0 %) were wearing headphones, talking on a mobile phone, and/or looking down at an electronic device. Headphone use was the most common distraction. Keywords Manhattan
Technology Distracted Pedestrians
Introduction Technology-related distracted behavior is an emergent national concern [1]. Based on a national review of hospital emergency room records, injuries related to mobile phone use in pedestrians increased tenfold from .37 % of pedestrian injuries in 2005 to 3.67 % in 2010 [2]. One national study concluded that, of headphone-related pedestrian-vehicle crashes, 29 % involved failure to respond to an audible warning [3]. In New York City (NYC), it has been estimated that pedestrian choices may cause *30 % of pedestrian fatalities [5]; 168 pedestrians were killed in NYC by cars in 2013 [4]. Just as using technology while driving is dangerous, listening to, looking at or talking into an electronic device while walking divides attention and increases the risk of injury [6–11], yet little is known about the frequency of these behaviors. The purpose of this study was to quantify technology-related distracted pedestrian behavior at five dangerous and busy intersections in Manhattan.
Methods C. H. Basch (&) Department of Public Health, William Paterson University, Wing 143, Wayne, NJ 07470, USA e-mail: [email protected] D. Ethan Health Education and Promotion Program, Lehman College, The City University of New York, Bronx, NY, USA P. Zybert Teachers College, Columbia University, New York, NY, USA C. E. Basch Health Education at Teachers College, Columbia University, New York, NY, USA
Pilot study data on distracted walking [12] was used to select the five highest density Manhattan intersections among those deemed most dangerous [13]. Borrowing methods of the pilot study [12], one coder tallied instances of the following pedestrian behaviors coming towards them: talking on a mobile phone, wearing headphones, looking down at a mobile device, engaging in a combination of these behaviors. Another coder tallied total pedestrian flow. At each intersection, data were collected at four times of the day: morning commute (7:30–9:30 a.m.), morning recreation (9:31 a.m.–12:29 p.m.), afternoon
123
J Community Health
recreation (12:30–4:29 p.m.), and evening commute (4:30–6:30 p.m.). Ten cycles of signal changes (i.e., ‘Walk’ to ‘Don’t Walk’) were observed at each of the four corners of each of the five intersections. At each intersection, ten signal cycles was equivalent to 15 min. Data for ‘Walk’ and ‘Don’t Walk’ signals were tallied separately, the latter including only pedestrians who initiated crossing when the signal was solid red. The unit of analysis was chosen to be the time-specific intersection (i.e.; n = 20; 5 intersections 9 4 times of day). The matched pair Wilcoxon signed rank test was used to determine if there were differences in the rates of distracted walking behavior during the ‘Walk’ and ‘Don’t Walk’ signals for each behavior separately (i.e., wearing headphones, looking down at device, and talking on a mobile phone) as well as for any distracted behavior. To assess inter-rater reliability, both coders simultaneously tallied either total pedestrian flow or distracted behaviors at each of the five intersections during ten ‘Walk’ signals. The two tallies were discrepant an average of under .5 % of total pedestrian flow per intersection. The Institutional Review Boards at William Paterson University, Lehman College, City University of New York, and Teachers College, Columbia University approved this study.
Table 1 Pedestrian volume at five Manhattan intersections during 40 light signal cycles, 10 per corner, by time of day Location 34th St and 6th Ave
34th St and 7th Ave
34th St and 8th Ave
42nd St and 6th Ave
42nd St and 8th Ave
Time of day
Number of Pedestrians
a.m. commute
725
a.m. recreation
852
p.m. recreation p.m. commute
878 1131
a.m. commute
1731
a.m. recreation
1459
p.m. recreation
1418
p.m. commute
1847
a.m. commute
958
a.m. recreation
684
p.m. recreation
722
p.m. commute
1189
a.m. commute
1321
a.m. recreation
531
p.m. recreation
579
p.m. commute
1083
a.m. commute
1163
a.m. recreation p.m. recreation
654 1049
p.m. commute
1786
Results The total number of pedestrians observed at each of the five intersections and four times of day are presented in Table 1. Each cell represents data pooled across 10 signal cycles (i.e. 15 min) and four corners (i.e. total time 1 h). A total of 21,760 pedestrians were observed: 5898 during a.m. commute, 4180 during a.m. recreation, 4646 during p.m. recreation, and 7036 during p.m. commute. The data are categorized by ‘Walk’ and ‘Don’t Walk’ signals in Table 2. In all 20 observation periods, any distracted behavior rates were higher during the ‘Don’t Walk’ signal than during the ‘Walk’ signal. With one exception, headphone use rates were higher during the ‘Don’t Walk’ signal then during the ‘Walk’ signal. Nearly one-third of those crossing on a ‘Walk’ signal were distracted (n = 5414, 27.8 %), with wearing headphones the most common distraction (n = 3119, 16.0 %) followed by looking down at a device (n = 1012; 5.2 %) and talking on a mobile phone (n = 793; 4.1 %). Nearly one-half of those crossing on a ‘Don’t Walk’ signal were distracted (n = 974; 42.0 %), with wearing headphones the most common distraction (n = 564; 24.3 %), followed by looking down (n = 197; 8.5 %) and talking (n = 125; 5.4 %). Rates of multiple distracted behaviors were 2.5 % during a ‘Walk’ signal and 3.8 % during a ‘Don’t Walk’ signal. Matched pair signed rank tests confirm that each
123
distracted walking behavior and any distracted walking behavior was more frequent during the ‘Don’t Walk’ signal than during the ‘Walk’ signal.
Discussion A significant number of pedestrians are injured and killed in traffic crashes locally and nationally. In the United States in 2010, 4280 pedestrians were killed and another 70,000 injured in traffic crashes [14] Over 70 % of the fatalities were in urban areas [14]. This study is the largest to date describing technologybased distracted walking behaviors in NYC. Nearly onethird of pedestrians crossing on the ‘Walk’ and almost onehalf crossing on the ‘Don’t Walk’ signal at five busy Manhattan intersections were engaged with an electronic device. The most prevalent distracted behavior was headphone use, a behavior that impairs the ability to register important audible warnings [3]. The pilot study on which this study was based was the first study of distracted walking in NYC. The rate of distracted walking during the ‘Walk’ signal was nearly the same in the pilot and current studies (28.8 vs. 27.8 %). The rate of distracted walking during the ‘Don’t Walk’ signal was higher in the current study than in the pilot (42 vs.
231 (24.5) 395 (25.2)
p.m. recreation
p.m. commute 3.92 p = .000
360 (33.5) 147 (25.6)
a.m. commute
125 (23.7) 245 (25.2)
p.m. recreation p.m. commute
a.m. recreation
115 (24.3)
a.m. recreation
316 (28.7)
p.m. commute 398 (32.2)
187 (29.3)
p.m. recreation
a.m. commute
144 (23.5)
a.m. recreation
414 (25.7)
p.m. commute 286 (32.5)
269 (21.9)
p.m. recreation
a.m. commute
427 (32.6)
90 (40.7)
50 (47.6)
37 (46.3)
44 (49.4)
26 (50.0) 43 (39.4)
28 (48.3)
39 (45.3)
31 (35.6)
42 (50.6)
36 (50.7)
36 (46.2)
81 (34.5)
71 (37.0)
55 (36.9)
77 (51.7)
43 (36.1)
49 (51.0)
58 (33.1)
38 (45.2)
3.81 p = .000
222 (14.2)
110 (11.7)
59 (10.3)
267 (24.9)
50 (9.5) 112 (11.5)
43 (9.1)
281 (22.8)
168 (15.2)
91 (14.2)
62 (10.1)
215 (24.4)
242 (15.0)
128 (10.4)
260 (19.8)
407 (25.7)
98 (9.7)
86 (11.0)
73 (10.8)
145 (22.6)
26 (31.0)
44 (19.9)
27 (25.7)
9 (11.3)
34 (38.2)
12 (23.1) 26 (23.9)
23 (39.7)
17 (19.8)
14 (16.1)
18 (21.7)
22 (31.0)
29 (37.2)
53 (22.6)
37 (19.3)
36 (24.2)
62 (41.6)
24 (20.2)
18 (18.8)
33 (18.9)
* Standardized test statistic, matched pair Wilcoxon signed rank test, Walk versus Don’t Walk (n = 20); 2-tailed
Signed rank test*
42nd St and 8th Ave
42nd St and 6th Ave
34th St and 8th Ave
550 (34.8)
230 (22.7)
p.m. commute
a.m. recreation
206 (26.3)
p.m. recreation
a.m. commute
159 (23.5)
a.m. recreation
34th St and 7th Ave
210 (32.8)
a.m. commute
34th St and 6th Ave
Don’t Walk n (%)
Walk n (%)
Walk n (%)
Don’t Walk n (%)
Wearing headphones
Any distracted behavior
Time of day
Location
3.14 p = .002
63 (4.0)
53 (5.6)
32 (5.6)
39 (3.6)
30 (5.7) 57 (5.9)
36 (7.6)
40 (3.2)
63 (5.7)
40 (6.3)
45 (7.3)
29 (3.3)
66 (4.1)
74 (6.0)
70 (5.3)
66 (4.2)
67 (6.6)
64 (8.2)
49 (7.2)
29 (4.5)
Walk n (%)
18 (8.1)
11 (10.5)
13 (16.3)
6 (6.7)
2 (3.8) 4 (3.7)
3 (5.2)
5 (5.8)
11 (12.6)
11 (13.3)
7 (9.9)
2 (2.6)
19 (8.1)
15 (7.8)
14 (9.4)
8 (5.4)
11 (9.2)
18 (18.8)
12 (6.9)
7 (8.3)
Don’t Walk n (%)
Looking down at device
Table 2 Technology-related distracted pedestrian behavior at five Manhattan intersections by time of day and Walk and don’t Walk signal
2.17 p = .030
71 (4.5)
52 (5.5)
25 (4.4)
26 (2.4)
33 (6.3) 48 (4.9)
28 (5.9)
35 (2.8)
57 (5.2)
32 (5.0)
25 (4.1)
19 (2.2)
80 (5.0)
49 (4.0)
56 (4.3)
34 (2.1)
46 (4.5)
40 (5.1)
24 (3.5)
13 (2.0)
Walk n (%)
17 (7.7)
7 (6.7)
15 (18.8)
3 (3.4)
7 (13.5) 9 (8.3)
2 (3.4)
7 (8.1)
5 (5.7)
5 (6.0)
6 (8.5)
3 (3.8)
3 (1.3)
8 (4.2)
3 (2.0)
2 (1.3)
6 (5.0)
9 (9.4)
5 (2.9)
3 (3.6)
Don’t Walk n (%)
Talking on mobile phone
J Community Health
123
J Community Health
26.3 %) [12]. The intersections in the current study were selected because they were the busiest among the most dangerous in Manhattan. All were in midtown. In a study in Seattle, with 1102 pedestrians observed at 20 intersections, the rate of distracted walking was nearly 30 %, and headphone use was the most commonly observed distraction [15]. In a study in San Francisco, rates of technology related distracted walking varied by intersection, but peaked at 18 % [16]. This study is limited by its cross-sectional design and by its focus on only five intersections. We did not collect data that would have allowed us to assess differences between pedestrians walking with or against traffic (a relevant concept, especially during rush hour in midtown Manhattan). Nevertheless, with data at each of four time periods at each of the five busiest and most dangerous Manhattan intersections, this is the largest study of pedestrian distracted walking behavior conducted to date. Cheever and colleagues suggest that mobile device overuse may be a form of psychological dependency [17] and psychological research is exploring ways to dampen the desire to constantly ‘‘check in’’ (and enhance the sense of alertness to one’s surroundings) [18]. Future research should focus on means to increase pedestrian cooperation in remaining alert to their surroundings when crossing busy intersections. It is recommended that initiatives be implemented to heighten public awareness of the risks of distracted walking.
References 1. Smith, D., Schreiber, K., Saltos, A., et al. (2013). Ambulatory cell phone injuries in the United States: An emerging national concern. Journal Safety Research, 47, 19–23. 2. Nasar, J., & Troyer, D. (2013). Pedestrian injuries due to mobile phone use in public places. Accident Analysis and Prevention, 57, 91–95. 3. Lichenstein, R., Smith, D., Ambrose, J., & Moody, L. (2012). Headphone use and pedestrian injury and death in the United States: 2004–2011. Injury Prevention, 18, 287–290.
123
4. NYPD: 16,059 pedestrians and cyclists injured, 178 killed in traffic in 2013. Street Blog WebSite. http://www.streetsblog.org/ 2014/01/31/nypd-16059-pedestrians-and-cyclists-injured-178-kil led-in-traffic-in-2013/. Accessed September 14, 2014. 5. Vision Zero Action Plan 2014. Vision Zero Web Site. http:// www.nyc.gov/html/visionzero/pdf/nyc-vision-zero-action-plan. pdf. Accessed August 28, 2014. 6. Stavrinos, D., Byington, K., & Schwebel, D. (2009). Effect of cell phone distraction on pediatric pedestrian injury risk. Pediatrics, 123, e179–e185. 7. Schwebel, D., Stavrinos, D., Byington, K., et al. (2012). Distraction and pedestrian safety: How talking on the phone, texting, and listening to music impact crossing the street. Accident Analysis and Prevention, 45, 266–271. 8. Hatfield, J., & Murphy, S. (2007). The effects of mobile phone use on pedestrian crossing behaviour at signalised and unsignalised intersections. Accident Analysis and Prevention, 39, 197–205. 9. Bungum, T., Day, C., & Henry, L. (2005). The association of distraction and caution displayed by pedestrians at a lighted crosswalk. Journal of Community Health, 30, 269–279. 10. Strayer, D., & Drews, F. (2007). Cell-phone–induced driver distraction. Current Directions in Psychological Science, 16, 128–131. 11. Hosking, S., Young, K., & Regan, M. (2009). The effects of text messaging on young drivers. Human Factors, 51, 582–592. 12. Basch, C., Ethan, D., Rajan, S., & Basch, C. (2014). Technologyrelated distracted walking behaviors in Manhattan’s most dangerous intersections. Injury Prevention, 20, 343–346. 13. Top Ten Intersections by Crash and Borough. Crashstat Web Site. http://crashstat.org/sites/default/files/dangerous/Top%20Ten% 20Intersections%20by%20Crash%20Type%20and%20Boro. pdf. Accessed August 28, 2014. 14. Department of Transportation (US), National Highway Traffic Safety Administration (NHTSA). Traffic Safety Facts 2010: Pedestrians. Washington (DC). http://www-nrd.nhtsa.dot.gov/ Pubs/811625.PDF. Accessed August 28, 2014. 15. Thompson, L., Rivara, F., Ayyagari, R., et al. (2013). Impact of social and technological distraction on pedestrian crossing behavior: An observational study. Injury Prevention, 4, 232–237. 16. Cooper, J. F., Schneider, R. J., Ryan, S., et al. (2012). Documenting targeted behaviors associated with pedestrian safety. Transportation Research Record, 2299, 1. 17. Cheever, N., Rosen, L., Carrier, L., & Chavez, A. (2014). Out of sight is not out of mind: The impact of weapons of mass distraction on anxiety. Computers in Human Behavior, 37, 290–297. 18. Rosen, L. (2012). iDisorder: Understanding our obsession with technology and overcoming its hold on us. New York, NY: Palgrave Macmillan.
