Orf 467 Fall 2012
Prof. Kornhauser
Uncongested Mobility for All,
While Improving Safety, Energy and Environmental Consequences:
New Jersey’s Area-wide aTaxi System
Final Orf 467 Class Project Presentations
Friday, January 11, 2013
10am – 1pm
Lunch will be served
Contents
1. Project Goals 4
2. Project Description 7
a. The project took as a given: 7
i. One “realization” of a typical weekday’s travel demand as generated by the Princeton Trip Synthesizer, some 30 million trips, each having a specific departure latitude, longitude and time, specific arrival latitude and longitude and an estimated time 7
ii. A simple array of aTaxiStand locations that covered the state. For simplicity, the state was “pixelated” into square pixels, 0.5 miles on a side. Longitude and latitude were converted into an Orf467HalfMileMeasure (O4HMM) whose units are: 7
iii. The New Jersey’s counties were divided into 8 areas each comprising of two or three counties. Pairs of students were then assigned the responsibility of first ascertaining the validity of the synthesized demand to appropriately represent a typical day o travel demand in their area. This included: 11
iv. Next, focus was placed on describing the character of how the aTaxis system would serve the demand for travel originating in each county. This includes: 13
v. Finally, focus was placed on describing the character of how the aTaxis system would serve the demand for travel throughout the State of New Jersey. This includes: 27
1. Project Goals
a. For years Ort 467 has been designing and analyzing State-wide PRT networks for New Jersey. Networks that consisted of roughly 10,000 stations interconnected by 10,000 miles of guideway used by 750,000 PRT vehicles capable of providing high-quality, non-stop, demand responsive service to 90% of the 30 million trips made in New Jersey on a typical day. Capital costs were optimistically estimated to be of the order of $150B and operating costs being arguably less than the marginal operating cost of today’s car. Unfortunately the demand analyses were crippled by demand data insufficiently précises enough spatially to properly evaluate the walk disutility associated with accessing the precise station locations nor sufficiently precise temporally to properly ascertain the ride-share, and thus efficiency opportunity, of demand-responsive PRT service.
Last year’s Orf467 began addressing the demand data shortcomings by building an individual’s activity-based trip synthesizer, Synthesizing Individual Trip ravel Demand in New Jersey. This trip synthesizer, enhanced by Talal Mufti in his MSR thesis, Chris Brownell in his ORFE BSE thesis and this year’s Orf467 class, generates demographic characteristics for each of the 8,791,894 individuals living in 118,654 census blocks that comprise New Jersey. Each individual’s demographic signature is used to draw from appropriate distributions the individual’s daily travel tour behavior as well the specific name and address of every establishment visited by each individual on a typical day. The spatial specificity of the census blocks characterizes the spatial accuracy of the home end of home-based trips. Geo-coded addresses of the non-home end(s) of each trip complete the spatial specificity. Arrival time and duration distributions for each type of establishment (school, workplace, restaurant, etc.) are used to randomly select precise departure times, in seconds-from-midnight, for every trip segment to provide the desired temporal specificity. For completeness, trips made by out-of-state residents working in New Jersey are also synthesized to yield a detailed spatial and temporal representation of the more than 30 million trips made to or from locations in New Jersey on a typical weekday.
The other “non-starter” of Area-Wide PRT is its dependence on an extensive dedicated guideway infrastructure required to interconnect all of the stations. While PRT stations can be readily be integrated in facilities that would embrace the premier accessibility and clientele connectivity afforded by such stations and residential areas tend to have centralized open space that make ideal station locations, the guideways are the real challenge. It is cost prohibitive to place them out of sight underground and considered unsafe to place at-grade. Overhead, while expensive, is technologically feasible, but architecturally and societally unacceptable. Architects have yet to be creative enough to design overhead guideways that are neighborhood friendly and no one wants an elevated guideway going anywhere near their bedroom window. Case closed!
However, the advent autonomous vehicles, like Pospect 12 or the Google car, presents the opportunity to address both shortcomings of PRT and have the opportunity to deliver PRT-like, and consequently auto-like, mobility to all throughout New Jersey. Consider an aTaxi (autonomousTaxi) system comprising of autonomous automated vehicles operating from designated aTaxiStands located throughout New Jersey at places similar to that of PRT stations whereby aTaxis would travel between aTaxiStands using New Jersey’s existing road and highway infrastructure. Since aTaxis are being designed to operate automatically in traffic with human-operated vehicles while not requiring any change in the existing roadway infrastructure, such a system can readily begin to operate effectively with but a single pair of aTaxiStands. From this beginning, additional aTaxi stands can readily be located/built where ever there exists sufficient customer demand. As aTaxiStands are added, the mobility afforded grows more quickly, especially at first because of the network connectivity effects, while only requiring the addition of aTaxiStands and not interconnecting guideway. This will allow the system to grow and evolve naturally. Much later, a point of diminishing return will undoubtedly be reached but this point seems to be so close to service for all that service for all can be offered at a very small additional cost. Importantly, the system has the opportunity to grow naturally or “virally” from austere beginning to serve much if not all of New Jersey.
While autonomous vehicles can evolve to be simply chauffeured versions of our existing cars, providing an even high form of mobility, in that they emancipate us from the driving function and make travelling substantially safer, they continue to carry the detrimental availability, congestion, environmental and energy side-effects of the existing automobile. Assembling a fleet of these vehicles offering on-demand mobility between aTaxiStands conveniently located nearby essentially anywhere anyone wants to go from and to in New Jersey may attract the patronage of enough customers to take many, if not most cars off the roads, especially during times when roadways are congested. If he demand is high and concentrated enough to allow for substantial casual sharing of aTaxi rides, then more manually driven cars would be taken off of congested roads than are being replaced by aTaxis, thus not only addressing the automobile’s detrimental attribute availability, but also reducing congestion, environmental impact and energy consumption.
The goal of this year’s Orf467 class was to ascertain a State-wide aTaxi system’s potential of the casual sharing of rides and thereby reduce congestion, environmental impacts and energy consumption.
2. Project Description
a. The project took as a given:
i. One “realization” of a typical weekday’s travel demand as generated by the Princeton Trip Synthesizer, some 30 million trips, each having a specific departure latitude, longitude and time, specific arrival latitude and longitude and an estimated time
ii. A simple array of aTaxiStand locations that covered the state. For simplicity, the state was “pixelated” into square pixels, 0.5 miles on a side. Longitude and latitude were converted into an Orf467HalfMileMeasure (O4HMM) whose units are:
1. number of half-mile segments north of the equator (the “Y” axis) and
2. number of half-mile segments east of Greenwich as they would exist at a latitude of 38o (the “X” axis) .
Translation of the origin to a point south west of the New Jersey boundary (-76oE, 38oN) allows the integer value of any point in the O4HMM coordinate system defined as:
X_Pixel = Int{X_coordinate = 108.907 * (lon + 76)}
Y_Pixel = Int{Y_coordinate = 138.2 * (lat – 38)}
(Translation to (-75.7, 38.9) instead of (76, 38) would yield a more compact “upper-right-hand-quadrant” covering of the New Jersey. Also, Int{} is assumed to “drop the decimals” rather than “round, or convert to nearest”)
to be a pointer into a pixel array.
The pixels can be displayed in Google Earth using by creating *.kml files of the gris points. A simple way is to create the points and lines to be drawn in Excel and then using Google’s ExcelToKml converted, http://www.earthpoint.us/ExcelToKml.aspx, they can be viewed in Google Earth. The grid points can be displayed by modifying the excel file http://www.princeton.edu/~alaink/Orf467F12/NJ_aTaxisOrf467F12/NJPixelBoundaries.xlsx that displays the boundary points of the rectangle that covers New Jersey:
Latitude / Longitude / Name / Description / Icon / Lat (X) / Lon (Y)38 / -76 / 0,0 / 98 / 0 / 0
38 / -73.88810637 / 230,0 / 98 / 230 / 0
38.91172 / -75.57762127 / 46,126 / 98 / 46 / 126
38.91172 / -73.88810637 / 230,126 / 98 / 230 / 126
41.36469 / -76 / 0,465 / 98 / 0 / 465
41.36469 / -75.57762127 / 46,465 / 98 / 46 / 465
41.36469 / -73.88810637 / 230,465 / 98 / 230 / 465
The 43,976 pixels contained in the array 46,126 by 230,465 cover New Jersey. The lower-left-corner of pixels in the Princeton area are show in figure## below.
Figure ## Pixel grid points near Princeton
Grid boundaries in the Princeton area are shown below.
Figure ### Pixel Boundaries in the Princeton Area
The horizontal boundary lines were drawn using the file http://www.princeton.edu/~alaink/Orf467F12/NJ_aTaxisOrf467F12/PixelLines1136-155_330-321.xlsx
a snipit of which is:
Latitude / Longitude / LineStringColor / Icon / IconColor / IconHeading / Lat (Y) / Lon (X)40.387844 / -74.57676733 / Yellow / 98 / 155 / 330
40.322721 / -74.57676733 / Yellow / 98 / 155 / 321
40.387844 / -74.58594948 / Yellow / 98 / 154 / 330
40.322721 / -74.58594948 / Yellow / 98 / 154 / 321
40.387844 / -74.59513163 / Yellow / 98 / 153 / 330
40.322721 / -74.59513163 / Yellow / 98 / 153 / 321
40.387844 / -74.60431377 / Yellow / 98 / 152 / 330
40.322721 / -74.60431377 / Yellow / 98 / 152 / 321
40.387844 / -74.61349592 / Yellow / 98 / 151 / 330
40.322721 / -74.61349592 / Yellow / 98 / 151 / 321
The Pixel containing the center of Princeton University, 146,324 is shown in Figure ###
Figure ## Pixel 146,324 Princeton University
By locating an aTaxiStand at the center of each O4HMM pixel, any trip end within the pixel can be reasonably assumed to be served by the nearest aTaxiStand which is most likely to be the one located at the center of the pixel. Thus, a simple coordinate transformation and intergerization converts any trip end latitude and longitude into a pointer to a unique trip end aTaxiStands. For example, a trip end at (40.264 N, -74.489 E) points to the pixel centered at (164.5, 312.5) on the grid, as it is 162.5 half-miles east and 312.5 half-miles north of the origin. The integer pair (164,312) can be used as a pointer into arrays of size X_Range x Y_Range to reference any data associated with this pixel, including the unique aTaxiStand which resides at the center of the pixel, in this example at (40.261216N, -74.489537E). (164,312) is thus the pointer to the aTaxiStand serving all trips ends in the 0.5 mile pixel containing (40.264 N, 74.489 W) as well as any location with -74.494128..< longitude < -74.484946.. and 40.257597.. < latitude < 40.264833..
iii. The New Jersey’s counties were divided into 8 areas each comprising of two or three counties. Pairs of students were then assigned the responsibility of first ascertaining the validity of the synthesized demand to appropriately represent a typical day of travel demand in their area. This included:
1. Population
2. Number of census blocks
3. Number of trips originating in each county(or number of trips made by county residents, whichever is easier to determine)
Table1: Summary of Population & Originating Typical Weekday TripsCounty / Population / Census Blocks / Originating Trips / Pixels w/Originations / Average Length of Originating Trips
name / # / # / # / # / miles
Atlantic / integer / integer / integer / integer / Single decimal
Bergen / integer / integer / integer / integer / Single decimal
… / integer / integer / integer / integer / Single decimal
Warren / integer / integer / integer / integer / Single decimal
State Total / integer / integer / integer / integer / Single decimal
External Zones
NYC / NA / NA / integer / integer / Single decimal
PHL / NA / NA / integer / integer / Single decimal
… / NA / NA / integer / integer / Single decimal
Rockland / NA / NA / integer / integer / Single decimal
External Total / NA / NA / integer / integer / Single decimal
Total (State +Externals) / NA / NA / integer / integer / Single decimal
4. Population versus census blocks, biggest first,
5. Cumulative distribution (0,1) of normalized county population versus census blocks, biggest first,
6. Description of the five (5) most populated census blocks and one sparsely populated block,