A comparison of three methods for assessing the walkability of the pedestrian environment

This paper presents the results of a research project conducted in the UK designed to increase understanding of the factors which influence levels of walking and pedestrian route choice. It describes a number of techniques that were used to assess the pedestrian environment from a pedestrian’s perspective. These techniques included a computer based tool developed using stated preference surveys to determine the relative values of a range of factors in the pedestrian environment; an on-street survey that was designed to investigate values and attitudes towards different attributes of the pedestrian environment along a route; and finally an ‘on the move survey’ where pedestrian volunteers were interviewed while walking along the route in order to get an actual account of their experiences as they walk. A case study is then used to show the benefits and disadvantages of using these different techniques and compares results for a pedestrian route in the City of Leeds in the UK. The results indicate that there were a number of pedestrian attributes considered important by pedestrians when walking including pavement cleanliness, safe crossing places, good connectivity and sense of security. The three approaches complement one another and have highlighted in different ways that the walking experience is affected by the cumulative impact of multiple interactions (both positive and negative) as people walk in the pedestrian environment.     

Walk-to-transit demand estimation methods applied at the parcel level to improve pedestrian infrastructure investment

Public transportation is a critical component of cities' transportation system that can be supported by a safe, complete, and connected pedestrian infrastructure. Agencies spend millions of dollars each year to improve transit ridership, yet many of the transit destinations do not have adequate pedestrian infrastructure to connect to the transit stops creating a substantial barrier to growing demand. This is particularly true in suburban areas. This paper presents a replicable methodology for estimating relative parcel-level transit demand such that analysts can conduct fine-grained evaluation and prioritization of the pedestrian network enhancements as they relate to public transit system. To this end, pedestrian infrastructure can boost transit ridership and enhance riders' safety. We rely on spatial data available in most cities coupled with land use and socioeconomic data to generate potential relative number of walk-to-transit trips for each parcel and weight the occupied road segments based on the results from mode choice and gravity models. Using this GIS-based tool, we identify road segments that have a higher potential in serving as a walking path to transit stops and prioritize gaps in existing sidewalk infrastructure. This result eliminates arbitrary sidewalk investment scoring programs and the reliance on transit walksheds to direct investment. We apply this method to a case study of the city of Knoxville and discuss the challenges and possible solutions. This approach can help city planners and engineers in data-oriented investment strategic management of sidewalk enhancement programs that support transit.     

Recreational traffic management: The relations between research and implementation

Traffic management in National Parks is necessary in order to avoid the negative effects of car traffic. Implementing traffic management measures can be difficult, due to public resistance or other problems. A research project in the Veluwezoom National Park in The Netherlands shows that there are two important reasons for problematic implementation. The first reason is a lack of knowledge about the recreational use of the area. The second reason is the interdependence of actors involved. Both aspects need to be considered and related to each other in order to make more realistic traffic management plans.     

Traffic barriers and pedestrian crossing behaviour

This paper is concerned with the relationships between traffic conditions and pedestrian behaviour which determine the extent of barrier effects. These relationships are examined on a high-density mixed-use street in central Edinburgh. Such streets are generally important routes for both pedestrians and vehicles, where there is maximum potential for conflicts between them. Crossing strategies and pedestrian trip-making activity are shown to be modified in response to changes in traffic conditions, notably to changes in traffic volumes and parking activity. Barrier effects are seen to vary markedly with the age group of pedestrians. The findings are related to pedestrian safety on such streets and to issues involved in the design and implementation of traffic management schemes. It is concluded that barrier effects on pedestrians should be a standard consideration in scheme design and implementation.     

Varying influences of the built environment on daily and hourly pedestrian crossing volumes at signalized intersections estimated from traffic signal controller event data

Direct-demand models of pedestrian volumes (identifying relationships with built environment characteristics) require pedestrian data, typically from short-duration manual counts at a limited number of locations. We overcome these limitations using a novel source of pedestrian data: estimated pedestrian crossing volumes based on push-button event data recorded in traffic signal controller logs. These continuous data allow us to study more sites (1494 signalized intersections throughout Utah, US) over a much longer time period (one year) than in previous models, including the ability to detect variations across days-of-week and times-of-day. Specifically, we develop direct demand (log-linear regression) models that represent relationships between built environment variables (calculated at ¼- and ½-mile network buffers) and annual average daily and hourly pedestrian metrics. We control spatial autocorrelation through the use of spatial error models. All results confirm theorized relationships: There is more pedestrian activity at intersections with greater population and employment densities, a larger proportion of commercial and residential land uses, more connected street networks, more nearby services and amenities, and in lower-income neighborhoods with larger households. Notably, we also find relevant day-of-week and time-of-day differences. For example, schools attract pedestrian activity, but only on weekdays during daytime hours, and the coefficient for places of worship is higher in the weekend model. K-fold cross-validation results show the predictive power of our models. Results demonstrate the value of these novel pedestrian signal data for planning purposes and offer support for built environment interventions and land use policies to encourage walkable communities.     

Toward a solar city: Trade-offs between on-site solar energy potential and vehicle energy consumption in San Francisco,California

This study demonstrates the trade-offs between vehicle energy consumption and on-site solar energy potential in a city landscape. While higher urban density may curb many of the problems associated with sprawl mainly by reducing vehicle travels and associated energy use, it can also limit on-site rooftop solar energy utilization due to more shade on rooftops in dense urban settings and less available rooftop area per person. Using travel survey, Geographic Information System (GIS) and Light Detection and Ranging (LiDAR) data, we estimated vehicle energy use and rooftop solar potential in the City of San Francisco as a case study and calculated possible offsetting effects between vehicle energy consumption and rooftop solar potential. Given the prevalence of gasoline-based vehicles and today's solar photovoltaic (PV) panel efficiency, vehicle energy use per capita appears to exceed energy generated by rooftop solar PVs per capita across all density ranges, especially in lower density environments. At the point when electric cars and advanced, highly efficient solar PV panels penetrate the market, the results change based on the combination of different technological options. A significant reduction of energy consumption can be achieved through the immediate and rapid spread of energy efficient technologies in vehicles and solar PVs along with the long-term effect from gradual urban densification.     

Estimating pedestrian and cyclist activity at the neighborhood scale

In most parts of the U.S., data on bicycle and pedestrian activity at the neighborhood scale are sparse or non-existent, despite the importance of such data for local planning. Here, a simple small-area estimation method is used to pair travel survey with land use and census data to estimate cyclist and pedestrian activity for census tracts in the state of California. This method is an improvement on fixed per-capita estimates of activity based only on regional or statewide averages. These activity estimates are then used to calculate the intensity of road use by cyclists and pedestrians, and crash rates for these road users. For California, the intensity of pedestrian and cyclist road use in urban census tracts is double that found in suburban tracts, while use in suburban tracts is an order of magnitude greater than that found in rural tracts. Per-capita estimates would suggest substantially smaller differences between neighborhood types. On the safety side, although non-severe crashes involving cyclists and pedestrians are much more likely in more urban areas, severe crash rates for the non-motorized modes exhibit no clear spatial pattern. The method used is simple and easily replicable, potentially filling a critical need for bicycle and pedestrian planners.     

Assessment of non-recurrent congestion caused by precipitation using archived weather and traffic flow data

Since precipitation has a negative impact on traffic congestion, there have been various studies for modeling the relationship between precipitation and its impact on traffic flow. However, due to limitations on existing data, none of the previous studies have accounted for the estimation of the total delay caused by precipitation. The objective of this study is to estimate the non-recurrent traffic congestion on freeways caused by precipitation. To accomplish this objective, archived weather and traffic data for the year 2008 from the Korean Freeway Systems was collected and analyzed. As a result, non-recurrent traffic congestion was about 1.6 million vehicle-hours due to rainfall and 186,000 vehicle-hours due to snowfall in 2008. In addition, simple analyses were performed to describe the average non-recurrent traffic congestion per unit distance as a function of precipitation and a function of the time period of precipitation. Although precipitation events might not be handled by human efforts, these results will assist in making strategic plans such as active speed management and contingency planning for mitigating traffic congestion due to precipitation.     

Exploring the relationship between truck load capacity and traffic accidents in the European Union

Applying econometric techniques to EU28 panel data and controlling for explanatory variables such as road types, we find that increased truck load capacity does not necessarily aggravate road traffic safety. Specifically, heavy trucks do not seem to be linked with greater numbers of traffic fatalities/accidents, medium trucks appear to be the worst performers in terms of fatalities, and light trucks seem to be the worst for accidents. In summary, our results clarify the complex relationship between truck load capacity and road safety, pointing to the existence of a negative correlation for accidents per capita and an inverse U-shaped curve for fatalities per capita.     

Effect of street network design on traffic congestion and traffic safety

We examine the effects of street network design on congestion levels and crash rates in neighborhoods across Utah's Wasatch Front. We employ propensity score matching to select pairwise neighborhood samples that have other similar characteristics but differ greatly in street network design. Our results show that denser and more connected neighborhoods have significantly lower congestion levels, but they do not have measurably lower (or higher) crash rates, presumably due to the prevalence of four-way intersections. This study can help guide data-driven decision making on street network design standards for many of the growing urban areas across the country and globe.     

Evaluation of the spatial pattern of logistics facilities using urban logistics land-use and traffic simulator

Despite the growing research interests on the spatial restructuring of logistics facilities that occurred in many cities around the world, the relationship between the spatial pattern of logistics land use and the level of externalities is far from being elucidated. We use the Urban Logistics Land-use and Traffic Simulator (ULLTRA-SIM), developed for the Tokyo Metropolitan Area, to evaluate the level of externalities that accompany different spatial distribution patterns of logistics facilities. The ULLTRA-SIM takes a novel approach to analyze the urban freight impacts through the simulations of logistics facility locations, urban logistics chains, and truck flow. The results indicate that, while the moderate concentration and deconcentration of logistics facilities do not significantly affect the level of externalities, scarcity of logistics facilities in or near the high demand locations exacerbates negative externalities. Also, the results of the simulations underscore the need for rigorous analysis in order to reduce negative externalities through logistics land use policies.     

Increasing the resilience of air traffic networks using a network graph theory approach

Air traffic networks are essential to today’s global society. They are the fastest means of transporting physical goods and people and are a major contributor to the globalisation of the world’s economy. This increasing reliance requires these networks to have high resilience; however, previous events show that they can be susceptible to natural hazards. We assess two strategies to improve the resilience of air traffic networks and show an adaptive reconfiguration strategy is superior to a permanent re-routing solution. We find that, if traffic networks have fixed air routes, the geographical location of airports leaves them vulnerable to spatial hazard.     

Representing pedestrian activity in travel demand models: Framework and application

There have long been calls for better pedestrian planning tools within travel demand models, as they have been slow to incorporate the large body of research connecting the built environment and walking behaviors. Most regional travel demand forecasting performed in practice in the US uses four-step travel demand models, despite advances in the development and implementation of activity-based travel demand models. This paper introduces a framework that facilitates the abilities of four-step regional travel models to better represent walking activity, allowing metropolitan planning organizations (MPOs) to implement these advances with minimal changes to existing modeling systems. Specifically, the framework first changes the spatial unit from transportation analysis zones (TAZs) to a finer-grained geography better suited to modeling pedestrian trips. The MPO's existing trip generation models are applied at this spatial unit for all trips. Then, a walk mode choice model is used to identify the subset of all trips made by walking. Trips by other modes are aggregated to the TAZ level and proceed through the remaining steps in the MPO's four-step model. The walk trips are distributed to destinations using a choice modeling approach, thus identifying pedestrian trip origins and destinations. In this paper, a proof-of-concept application is included to demonstrate the framework in successful operation using data from the Portland, Oregon, region. Opportunities for future work include more research on the potential routes between origins and destinations for walk trips, application of the framework in another region, and developing ways the research could be implemented in activity-based modeling systems.     

A new zone system to analyze the spatial relationships between the built environment and traffic safety

This study aims to investigate the impacts of the built environment on traffic safety at a zonal level using a newly developed crash-related zone system. Traffic analysis zones (TAZs) have been widely employed to analyze traffic safety at a macroscopic level. However, this zone system use may present problems. Unlike previous studies, in which new zoning systems were created from aggregating TAZs, in this study the new zone system, formed by traffic safety analysis zones (TSAZs), is created from the smallest available census units. Geographically Weighted Negative Binomial Regression (GWNBR) models are used and a comparative analysis between non-spatial global crash prediction models and spatial local GWPR (Geographically Weighted Poisson Regression) and GWNBR models using the two zonal systems is presented. We find that TSAZs based models performed better than TAZs based models, especially when combined to the GWNBR technique. Our results show that several features of the built environment are significant crash predictors, and that the relationships among these features and traffic safety vary across space. By combining a crash-related zonal system with spatial GWNBR models to understand the built environment effects on traffic safety, the results of the analysis can help urban planners to consider traffic safety proactively when planning or retrofitting urban areas.     

A review of deterministic, stochastic and hybrid vehicular exhaust emission models

Vehicles spend more time near junctions and intersections in different driving modes, i.e., queuing, decelerating or accelerating and thus generating more pollutants than at road links [Claggett, M., Shrock, J., Noll, K.E., 1981. Carbon monoxide near an urban intersection. Atmos. Environ. 15, 1633-1642]. As a result, the receptors in these urban corridors are prone to frequent exposures of high pollutant concentrations (episodic conditions). In order to predict such ‘episodes’, an air quality model, capable of estimating the entire range (middle and extremes) of pollutant concentration distribution is needed. Hybrid models (combining deterministic and statistical distribution models) have demonstrated the ability to predict the entire range of pollutant concentrations in such co mplex dispersion situations with reasonable accuracy [Jakeman, A., Simpson, R.W., Taylor, J.A., 1988. Modelling distributions of air pollutant concentrations-III: Hybrid modelling deterministic-statistical distributions. Atmos. Environ. 22 (1) 163-174]. The present paper reviews the relevant deterministic and stochastic based vehicular exhaust emission models that may be hybridized and thus generate a hybrid model with improved prediction accuracy. The paper also describes the implications of hybrid models in formulating the Episodic-Urban Air Quality Management Plan (e-UAQMP).     

Transit-oriented development,integration of land use and transport,and pedestrian accessibility: Combining node-place model with pedestrian shed ratio to evaluate and classify station areas in Lisbon

Transit-oriented development is being actively promoted as an urban design model for areas around transit stations. In addition, planning for accessibility is being promoted, which requires integrating land use with transportation planning, and to match the transportation features with the intensity and diversity of land use of the station areas. Nevertheless, and despite the evident similarities between the two approaches, an integrated evaluation tool of a station area in terms of its transportation, land use, and urban design features is missing. In this paper, we bring into the literature on integration of land use and transport a key feature of the transit-oriented development literature: the urban design features of the station areas, in particular their pedestrian friendliness. By complementing the node-place model with an evaluation of the pedestrian connectivity of station areas of Lisbon, we combine these two perspectives in order to evaluate and classify station areas in three different aspects: land use, transportation, and walkability conditions. Our results show that a balanced node-place is not necessarily a transit-oriented development, and vice versa, and so a complementary analysis of both is useful to identify and classify a station area. Therefore, we suggest a typology of station areas based on the three components, which might be used as a planning tool for the development of the station areas into balanced transit-oriented development areas.     

Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data

Traffic crashes are geographical events, and their spatial patterns are strongly linked to the regional characteristics of road network, sociodemography, and human activities. Different human activities may have different impacts on traffic exposures, traffic conflicts and speeds in different transportation geographic areas, and accordingly generate different traffic safety outcomes. Most previous researches have concentrated on exploring the impacts of various road network attributes and sociodemographic characteristics on crash occurrence. However, the spatial impacts of human activities on traffic crashes are unclear. To fill this gap, this study attempts to investigate how human activities contribute to the spatial pattern of the traffic crashes in urban areas by leveraging multi-source big data. Three kinds of big data sources are used to collect human activities from the New York City. Then, all the collected data are aggregated into regional level (ZIP Code Tabulation Areas). Geographically Weighted Poisson Regression (GWPR) method is applied to identify the relationship between various influencing factors and regional crash frequency. The results reveal that human activity variables from multi-source big data significantly affect the spatial pattern of traffic crashes, which may bring new insights for roadway safety analyses. Comparative analyses are further performed for comparing the GWPR models which consider human activity variables from different big data sources. The results of comparative analyses suggest that multiple big data sources could complement with each other in the coverage of spatial areas and user groups, thereby improving the performance of zone-level crash models and fully unveiling the spatial impacts of human activities on traffic crashes in urban areas. The results of this study could help transportation authorities better identify high-risky regions and develop proactive countermeasures to effectively reduce crashes in these regions.     

Temporal aggregation and spatio-temporal traffic modeling

Traffic forecasting is crucial for policy making in the transport sector. Recently, Selby and Kockelman (2013) have proposed spatial interpolation techniques as suitable tools to forecast traffic at different locations. In this paper, we argue that an eventual source of uncertainty over those forecasts derives from temporal aggregation. However, we prove that the spatio-temporal correlation function is robust to temporal aggregations schemes when the covariance of traffic in different locations is separable in space and time. We prove empirically this result by conducting an extensive simulation study on the spatial structure of the Milan road network.     

Handling the pseudo pilot assignment problem in air traffic control training by using NASA TLX

Simulator courses play an important role in a department training air traffic controllers (ATCOs). One of the most important elements of these courses is a pseudo-pilot (PP) who has active tasks during simulator training. At Eskisehir Technical University (ESTU) PP assignments are made manually to meet the demand of related courses by considering the availability of PP. Even where it is attempted to assign each PP equally in terms of period, personal workloads differ due to the different psychological (mental) and physiological requirements of the simulator tasks. In this study, the PP assignment problem is investigated using mixed-integer programming (MIP). For this purpose, firstly, an equal period assignment to pilots was attempted with a mathematical model, called the Equality of Periods Model (EPM). Then, simulator tasks were weighted using the NASA Task Load Index (NASA TLX), and an Equality of Workload Model (EWLM) was created based on these weights. Finally, these models were combined to make fair assignments with the Fair Model (FM). The results indicate that the proposed models significantly reduce the differences of workload and working period compared to manual assignment (MA).     

A GIS-based method for evaluating the walkability of a pedestrian environment and prioritised investments

Despite the large investments made in the construction and modernisation of railway infrastructure, poor quality pedestrian routes may discourage users from using public transport. In fact, very little attention is generally paid to pedestrian mobility. Therefore, a method for evaluating the quality of pedestrian paths and the accessibility to railway stations has been developed. This method considers the main factors influencing the walkability of an urban area and makes it possible to establish the priorities for intervention, i.e. to identify the arcs of a pedestrian network that require prioritised action. The methodology is a decision support tool that can be used by policymakers and is developed in a GIS environment. Three railway stations in Palermo and its surrounding areas were chosen as a case study.