Safety and Security Issues with Non-motorized Transportation: An Examination of Potential Planning, Design and Technology Solutions Stephen T. Vaughn University of Illinois at Chicago IGERT Presentation April 30, 2009

Over 60% of all personal trips are 5 miles or less in trip length with 40% of those trips 2 miles or less. These are considered reasonable bicycling distance. (2001 NHTS) 14% of all personal trips are a ½ mile or less, which is considered reasonable walking distance. (2001 NHTS)

Percentage of Daily Personal Trips A total of 65.1% of work trips are within walking/bicycling distance Work  18.9% < 1 mile  24.0% 1 to 3 miles  22.2% 3 to 6 miles A total of 43.7% of non-work trips are within walking or bicycling distance Non-work  8.8% < 1 mile  15.0% 1 to 3 miles  19.9% 3 to 6 miles

Daily travel by Walking and Bicycling

Walking and Bicycling as a share of all modes l_report/

Is a sedentary lifestyle preferred to a physically active one?

Survey results find that 80% of the American public would like to walk more for exercise. 78% would like to walk more for fun 63% would like to walk more to stores and to run errands 38% would like to walk to work more 79% consider presence of sidewalks and walkable communities when deciding where to live National Highway Traffic Safety Administration “National Survey of Bicyclist and Pedestrian Attitudes and Behavior”

So why isn’t there more use of non-motorized transportation?

Survey on bicycling as a mode of travel National Highway Traffic Safety Administration “National Survey of Bicyclist and Pedestrian Attitudes and Behavior”

More reasons for sedentary lifestyles? Reasons youth are not walking or bicycling to school: School is too far away (66%) *No safe route-traffic (17%) *Fear of abduction (16%) *Neighborhood crime (6%) Lack of convenience (15%) Children don’t want to walk (6%) 39% of the results deal with SAFETY concerns

 Illinois Department of Transportation Accident variables and description of crash data  Chicago Police Department Incident data such as gender of victim and criminal, type of crime, etc.  UTC Spatial Decision Support System Environmental factors such as housing and transit quality, census data, transportation accessibility

Physical Environment Factors FunctionalitySafetyAestheticDestination Crossing aids Crossings Lighting Verge width Surveillance Cleanliness Sights Garden maintenance Parks Pollution Trees Architecture Street Maintenance Direct route Gradient Intersection design Intersection distance Kerb type Other access points Path continuity Path design Path location Path maintenance Path surface Path width Street design Street type Street width Traffic control devices Traffic speed Traffic volume Type of path Vehicle parking Local facilities Parks Public transport Services Shops Vehicle parking facilities Bike parking facilities Reference: Pikora, Giles-Corti, Bull, Jamrozik, Donovan (2003) Developing a framework for assessment of the environmental determinants of walking and cycling. Social Science and Medicine. pp

Traveler Safety and Security Issues Roadway Network Functionality Number of street lanes Width of lanes Roadway average daily traffic Availability of bike lanes/paths Roadway speed limit Presence of signage Traffic control devices Presence of on-street parking Condition of pavement (CRS) Curb type Presence of sidewalks and pavement Number of access points Personal Safety and Security Weather Special Events Crosswalks Presence of pedestrian signals Speed of travel Street lighting Crime Incidences* Accident Hotspot Identification*

Traveler Safety and Security Issues Non-motorized transportation choice Roadway Network Functionality Personal Safety and Security Risk Exposure Model

width of roadway # of street lanes ADT access points traffic volume bike lanes signage weather curb type sidewalks neighborhood crime on-street parking street lighting accident hotspot special events bike path traffic control device pavement speed limit cross walks road surface condition

Traveler Information Network Roadway Network Functionality Personal Safety and Security Information HUB (risk exposure model) Traveler Preference Transit Stations Website PDA’sCell PhonesBike Stations (Real time info) (Static/Historical info)

What is Historical (Static) and Real time information? Historical (Static) Information Number of street lanes Width of lanes Roadway average daily traffic Availability of bike lanes/paths Roadway speed limit Presence of signage Traffic control devices Street lighting Presence of on-street parking Condition of pavement (CRS) Curb type Presence of sidewalks and pavement Number of access points Crime Incidences* Accident Hotspot Identification* Real time information Weather Special Events Transit arrival/departure time Congestion instances Speed of travel Estimated time of arrival

What is traveler preference? Aesthetic Risk Tolerance Destination Safest Route Traveler Prompt Information HUB

What is Risk Tolerance? Risk averse: traveler would prefer the safest route with less risk exposure as possible and possibly the longest travel time. Risk neutral: traveler is indifferent to route of travel Risk seeking: traveler prefers the fastest route to their destination

Why are perceptions of crime important? “fear and concern about crime are related to perceptions of uncivil behavior” (Lewis and Maxfield, 1980)

Crime Identification (Neighborhood Perception) Crime type: manslaughter homicide criminal sexual assault involuntary aggravated assault aggravated battery simple assault simple battery offense against family (kidnapping child abductions/stranger)

Accident Hotspot Identification

What is a hotspot? At certain sites, the level of risk will be higher than the general level of risk in surrounding areas. Crashes tend to be concentrated at these relatively high-risk locations. Locations that have an abnormally high number of crashes are called hotspots or black spots.

Accident Classifications  Type A: Incapacitating injury which prevents the injured person from walking, cycling or driving (broken limbs, skull, chest injuries)  Type B: Injuries that are visible to observers at the scene (lump on head, bruises, lacerations)  Type C: Injuries that are reported and claimed but not evident (momentary unconsciousness)  Fatalities  Property Damage Only

Example of Accident Hotspot Identification Accidents in a roadway network

What is “Clustering”? Assumes that road accidents are spatially dependent when occurring in similar areas or because of shared common causes Implies a common causal factor

What mapping technologies are currently available for non-motorized transportation routing and how good are they?

Current routing programs (walking) (tele-atlas 2009)

Current routing programs (by car) (tele-atlas 2009)

Current routing programs (Navteq 2009)

Other data needs… Sidewalks and pavement information Construction updates (is this possible??) Crime data

Possible outcomes of research Safest route based on risk tolerance Social networking Inclement Weather re-routing Special events re-routing Increased trip linking availability

Questions Thank you….