Interpreting Accident Statistics

Accident statistics have often been used to support the argument that an abnormally small proportion of drivers account for a large proportion of the accidents. This paper compares statistics developed from six-year data for 7, 800 California drivers with results predicted using compound Poisson models for driver accident involvement that assume specific variations in accident likelihood among drivers. The results indicate that the fraction of drivers accounting for various proportions of all accident involvements is too high to suggest that "chronic" accident repeaters are involved in most accidents.National Science Foundation under Grants GK- 1685 and GK- 1647

Designing Equitable Merit Rating Plans

Throughout the United States it is common practice for automobile insurance premiums for a particular policy to vary depending upon the driver class and geographic location of the policyholder as well as the type and number of vehicles covered by the policy. In addition, most states also permit so-called "merit rating" plans whereby each policyholder's annual premium is adjusted up or down depending upon the insured's claims experience and traffice violation record during previous years. Although these merit rating plans may be viewed as a special type of risk classification, the rationale underlying their use is quite different from the justification for driver class and territory differentials. This paper develops a methodology for evaluating merit rating plans that are used in conjunction with other risk classification criteria. A theoretically equitable merit rating plan is designed and compared with plans commonly used throughout the country. The differences are striking, especially among high risk classes. For example, most typical merit rating plans overcharge good drivers in high risk classes -- often by more than 25%. 1.0 The Purpose of Merit-Rating The popular rational behind merit-rating is straightforward: Merit-Rating keeps "good" drivers from subsidizing "bad" drivers and, because having any accident or traffic violation means higher rates, it promotes safe driving an

Vehicle miles traveled and the built environment: evidence from vehicle safety inspection data

This study examines the linkage between household vehicle usage and their residential locations within a metropolitan area using a newly available administrative dataset of annual private passenger vehicle safety inspection records (with odometer readings) and spatially detailed data on the built environment. Vehicle miles travelled (VMT) and a set of comprehensive built-environment measures are computed for a statewide 250×250 m grid cell layer using advanced geographic information systems and database management tools. We apply factor analysis to construct five factors that differentiate the built-environment characteristics of the grid cells and then integrate the built-environment factors into spatial regression models of household vehicle usage that account for built environment, demographics, and spatial interactions. The empirical results suggest that built-environment factors not only play an important role in explaining the intraurban variation of household vehicle usage, but may also be underestimated by previous studies that use more aggregate built-environment measures. One-standard-deviation variations in the built-environment factors are associated with as much as 5000-mile differences in annual VMT per household. This study also demonstrates the potential value of new georeferenced administrative datasets in developing indicators that can assist urban planning and urban management.United States. Dept. of Transportation (Region One University Transportation Center Grant MITR21-4)Singapore-MIT Alliance for Research and Technology (Singapore. National Research Foundation

The Effect of Removing Accidents Repeaters From the Road

In newspaper editorials, public commentaries and the like, licensing authorities are often advised to solve the "accident problem" by taking the "nut behind the wheel" off the road. This paper uses six-year driver records of some 8, 000 California drivers to estimate the reduction in accidents that would in fact occur if accident repeaters were removed from the road

A large-scale study on predicting and contextualizing building energy usage

In this paper we present a data-driven approach to modeling end user energy consumption in residential and commercial buildings. Our model is based upon a data set of monthly electricity and gas bills, collected by a utility over the course of several years, for approximately 6,500 buildings in Cambridge, MA. In addition, we use publicly available tax assessor records and geographical survey information to determine corresponding features for the buildings. Using both parametric and non-parametric learning methods, we learn models that predict distributions over energy usage based upon these features, and use these models to develop two end-user systems. For utilities or authorized institutions (those who may obtain access to the full data) we provide a system that visualizes energy consumption for each building in the city; this allows companies to quickly identify outliers (buildings which use much more energy than expected even after conditioning on the relevant predictors), for instance allowing them to target homes for potential retrofits or tiered pricing schemes. For other end users, we provide an interface for entering their own electricity and gas usage, along with basic information about their home, to determine how their consumption compares to that of similar buildings as predicted by our model. Merely allowing users to contextualize their consumption in this way, relating it to the consumption in similar buildings, can itself produce behavior changes to significantly reduce consumption.National Science Foundation (U.S.) (NSF Computing Innovation Fellowship

The Effect of the 18-Year Old Drinking Age on Auto Accidents

The effect of Massachusetts' reduced drinking age on auto accidents is examined by employing an interrupted time series analysis of monthly accident data covering the period January, 1969, through September 1973. The data were stratified by driver age, accident type and (to a limited extent) operating-after-drinking. These raw data were adjusted using monthly mileage and seasonal indices and, where possible, a control group not affected by the drinking law. Correlograms of the adjusted series were computed to check for remaining systematic bias. Finally, the average accident rates for the adjusted, well-behaved series before and after the March 1973 change were compared using standard t-tests. Accident rates among 18-20 year olds did increase significantly-- about 40% for involvement in fatalities. Nevertheless, the results are consistent with the hypothesis that, as a result of the reduced drinking age, 18-20 year old driving-after-drinking behavior has become comparable to that of older drivers.This research was supported in part by NSF Grant GI 38004, by the MIT Undergraduate Research Opportunities Program, and by the U. S. Army Research Office (Durham) under Contract No. DA HC04-73-C-003

Understanding the Link between Urban Activity Destinations and Human Travel Pattern

URL to abstract on conference site. You have to be a conference participant to access papers.In the urban transportation field, planners and engineers have explored the relationship between urban destinations and travel behavior for more than half a century. However, we still have only a preliminary understanding about how the spatial arrangement of different types of urban activity destinations influence human travel, and how urban development policies influence travel patterns. Recent developments in urban sensing and cell phone technologies have enabled spatially-detailed and massive GIS-based datasets on land use, points of interest (POIs), cell phone and GPS-based tracking, etc. These new datasets provide rich possibilities for better understanding and modeling of urban activity patterns and travel behavior. In this study, we utilize such spatially-detailed data—POI data and large-scale travel tracker data—to explore the link between urban activity destinations and human travel patterns. First, we employ the timely and large-scale urban activity-based travel survey for the Chicago Metropolitan Area, implemented from January 2007 to February 2008 (Chicago Metropolitan Agency for Planning 2008). We derive travel patterns for both commute and non-commute activities in the Chicago Metropolitan Area for individuals and groups of individuals with various socioeconomic characteristics. The Chicago activity-based travel survey includes 10,552 households (23,452 individuals) who participated in either a one-day or two-day survey, providing detailed travel information for each household member for a particular assigned travel day(s). Second, we combine a spatially-detailed business establishment dataset (the InfoUSA 2008 data) included in the ESRI Business Analyst Package (ESRI 2009) and the employment data in the U.S. census to analyze the spatial distribution of urban activity destinations in the Chicago Metropolitan Area. Finally, we examine the linkage between these two aspects, and demonstrate the impacts of spatial arrangement of urban activity destinations on human travel patterns in the urban settings. This new study is crucial to understanding how the spatial patterns of urban activity destinations influence individuals’ and groups of individuals’ travel patterns at both individual and aggregated level. It is also important for policy making in the fields of urban development and transportation planning.MIT-Portugal ProgramMassachusetts Institute of Technology. Dept. of Urban Studies and Plannin

Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development.Singapore. National Research Foundation (through the Singapore-MIT Alliance for Research and Technology (SMART) Center for Future Urban Mobility (FM))Center for Complex Engineering Systems at MIT and KACS

Discovering urban spatial-temporal structure from human activity patterns

Urban geographers, planners, and economists have long been studying urban spatial structure to understand the development of cities. Statistical and data mining techniques, as proposed in this paper, go a long way in improving our knowledge about human activities extracted from travel surveys. As of today, most urban simulators have not yet incorporated the various types of individuals by their daily activities. In this work, we detect clusters of individuals by daily activity patterns, integrated with their usage of space and time, and show that daily routines can be highly predictable, with clear differences depending on the group, e.g. students vs. part time workers. This analysis presents the basis to capture collective activities at large scales and expand our perception of urban structure from the spatial dimension to spatial-temporal dimension. It will be helpful for planers to understand how individuals utilize time and interact with urban space in metropolitan areas and crucial for the design of sustainable cities in the future.Massachusetts Institute of Technology. Dept. of Urban Studies and PlanningUnited States. Dept. of TransportationSingapore-MIT Alliance for Research and Technology Cente