Green accessibility: Estimating the environmental costs of network-time prisms

Network-time prisms are powerful measures of space-time accessibility within transportation networks.  However, they fail to capture the environmental costs of potential mobility.  In this paper, we present a method for estimating the expected energy consumption and emissions associated with network time prisms.  We also verify our method using data from instrumented vehicles moving within an experimental network-time prism in the Phoenix AZ road network.

Song, Y., Miller, H.J., Stempihar, J. and Zhou, X. (2017), “Green accessibility: Estimating the environmental costs of network-time prisms for sustainable transportation planning,” Journal of Transport Geography, 64, 109-119.

Abstract.  Accessibility, or the ease to participate in activities and obtain resources in a given environment, is crucial for evaluating transportation systems. Greater accessibility is often achieved by increasing individuals’ potential mobility. However, potential mobility, if realized by motorized modes, can also generate negative environmental impacts such as fossil fuel consumption and greenhouse gas (GHG) emissions. While the negative environmental impacts of greater mobility are acknowledged, there has been a lack of research to validate those impacts using empirical data, especially considering variations in individuals’ mobility levels. This paper presents a method for estimating the expected environmental costs of accessibility represented by a network-time prism (NTP). A NTP delimits all accessible locations within a network and the available time for an individual to present at each location given a scheduled trip origin and destination, a time budget and the maximum achievable speeds along network edges. Estimating the expected environmental costs of a NTP involves three steps: (1) semi-Markov techniques to simulate the probabilities to move along network edges at given times; (2) the speed profiles for reachable edges, and (3) a cost function that translates speeds into environmental impacts. We focus on air quality and employ the motor vehicle emission simulator MOVESLite to estimate the CO2 emissions at both the edge and prism levels. We calibrate and validate the methods for experimental NTPs defined within the Phoenix, AZ, USA road and highway network using vehicles instrumented with GPS-enabled onboard diagnostic devices (OBD). We demonstrate the effectiveness of our method through two scenarios and investigate the impact of changes in mobility levels on the expected CO2 emissions associated with the experimental NTPs.

Keywords: Space-time accessibility; Network-time prism; Emissions

Columbus Wins USDOT Smart City Challenge!

Columbus is the winner of the US Department of Transportation’s Smart City Challenge, beating out San Francisco, Austin, Portland, Kansas City, Denver and Pittsburgh.   Columbus will receive a $40 million federal grant combined with $10 million from Vulcan Inc. and matched by an additional $90 million from private sector partners.  The award will invest in next generation transportation technologies and services,  including driverless vehicles, advanced traffic analytics and intelligent infrastructure.

Big news – a real game-changer for Columbus!

News articles:

Modeling Visit Probabilities within Network-Time Prisms Using Markov Techniques

Ying Song, Harvey J. Miller, Xuesong Zhou and David Proffitt (2016) “Modeling visit probabilities within network time prisms using Markov techniques,” Geographical Analysis, 48, 18-42.

The space-time prism is a key concept in time geography that captures both spatial and temporal constraints on an object’s potential mobility. For mobility within transportation networks, network-time prisms (NTPs) delimit accessible locations with respect to time given scheduling constraints, movement constraints, and speed limits imposed by the network. The boundary of a NTP has been used as a measure of individuals’ accessibility within a network. However, the interior structure has lacked quantitative characterization, including the distribution of visit probabilities at accessible locations. This article models visit probabilities within NTPs using two types of Markov techniques: (1) Brownian motion on undirected graphs for nonvehicular mobility (e.g., walking) and (2) continuous-time semi-Markov process for vehicular mobility (e.g., biking, driving). Based on these methods, we simulate nonvehicular and vehicular visit probabilities and visualize these distributions. For vehicular mobility, we compare the simulated visit probabilities with empirical probabilities derived from trajectories collected by Global Positioning System (GPS) in New York City, USA. The visit probabilities provide a quantitative description of individuals’ potential mobility within a NTP and a foundation for developing the refined accessibility benefit and cost measures that go beyond the binary nature of classical NTPs.