Socially-Aware Evaluation Framework for Transportation

There has been much previous work in the area of transportation modeling and traffic route choices [8, 9, 10, 11]. Understanding the distribution of traffic on local road network gained popularity in the past decade with route guidance systems rerouting traffic differently than before [12, 13, 14]. Most route guidance systems aim to provide an user with the least travel time route [15, 16]. This might mean taking people off the highway to local streets to save few minutes of travel time [17]. Some services provide routes to users based on shortest fuel consumption. This could mean travelling at a consistent speed and thus prefer certain kinds of roads that maintains that speed limit [18, 19]. Studies evaluating the impacts of navigation systems model app based and non app based users differently to understand the resulting congestion patterns [20, 21]. Most of them model a corridor or a small network with varying percentages of app users to show the increase in congestion as app users increase by using metrics like traffic flow, distance, and average marginal regret. Ahmed and Hesham modelled eco-routing as a feedback user equilibrium model for downtown LA and measured the outcome in terms of fuel consumption and congestion levels [22]. It could be seen from the previous studies that different strategies has different impacts across the city network in terms of time and distance. However no previous studies have explored the impacts of routing holistically on multiple city dimensions.

We conducted an extensive review of transportation literature to identify frameworks or indicators that can be used to assess the impacts of traffic routing strategies on cities. Due to lack of specific frameworks designed for routing, we reviewed the general frameworks in the transportation domain and assessed its usability for routing evaluation.

European Commission’s CITYkeys framework developed a set of city performance measures at city level and project level [23]. The framework was focused on five major themes of people, planet, prosperity, governance, and propagation. Out of the 116 key performance indicators, the ones concerning transportation were in car waiting time, reduction in traffic accidents, quality of public transportation, improved access to vehicle sharing solution, extended bike route network, reduced exposure to noise pollution, reduction in annual energy consumption. HASTA framework measures sustainability for a transportation project based on three dimensions of economic, environmental and social and six sustainability indicator groups for Swedish cities [24]. They came up with a total of 83 indicators. Additionally, considerable work is ongoing through the International Standards Organization (ISO), European Committee for Standardization, and BSI to establish proper standards in smart urban development project evaluation. ISO 37122:2019 provides a framework for resilient city with 19 themes and multiple indicators for each theme. The transportation theme has 14 indicators, mostly focusing on real-time technology, electric fleet, and integrated payment systems (ISO 37122:2019). In addition to the frameworks, there is a vast literature on transportation indicators used for evaluation of new projects. Specific to smart cities and transportation, Orlowski and Romanowska developed a set of smart mobility indicators that encompass the following domains: technical infrastructure, information infrastructure, mobility methods, and vehicles used for this purpose and legislation with 108 metrics [25]. Benevolo et al. (2016) investigated the role of ICT in supporting smart mobility actions, influencing their impact on the citizens’ quality of life, and on the public value created for the city as a whole [26]. They provided an action taxonomy considering three aspects of smart mobility actors including the main agents moving the smart initiatives, the use and intensity of ICT and the benefits of smart mobility actions on smart goals , to investigate the role of ICT in supporting smart mobility actions, influencing their impact on the citizens’ quality of life and on the public value created for the city as a whole.

The existing frameworks cannot be directly used for routing evaluation as they are general, considering the entirety of transportation or entirety of smart cities. Only few works have reasoned on more complex aspects, such as how different indicators interact reciprocally, what benefits they generate, how they affect citizens’ quality of life, what problems they solve and what is created. Further, they do not capture factors specific to the impact of routing strategies on cities. While the aspects of safety and neighborhood are discussed in some metrics, it is mostly qualitative and subjective in nature. Key challenges are centered on selecting suitable evaluation methodologies to evidence urban value and outcomes, addressing city’s objectives. With this focus, our framework grounded on literature develops a set of themes and indicators for cities to evaluate impacts of routing. To the best of our knowledge, this is the first time a holistic evaluation using multiple themes is used to identify the impacts of different routing strategies for a large scale network with cross city comparisons.

To explore the city level impacts of various routing strategies, we develop a holistic framework called Socially- Aware Evaluation Framework for Transportation - SAEF. We define the term ’socially-aware’ to include five complementary themes of neighborhood, safety, mobility, equity and environment (Figure 1).The themes are assembled from a set of city performance indicators grounded in literature [27, 28, 24]. Each theme identifies factors that are likely key concerns for that theme. For example, accidents are a concern when considering safety, similarly emissions are a concern when considering environmental quality. Based on these factors, we defined indicators/metrics (Figure 2) that could be derived from real world or simulated data. These indicators were culled from a thorough review of literature from domestic and international, city policies and international organizations related to transportation. A list of over 100 indicators with wide range of scale and use were identified and a manageable set of indicators that we assessed as being important as well as measurable were created for each theme. This resulted in 23 indicators: 6 were selected as reflections of neighborhood quality, 4 for safety, 6 for mobility, 4 for equity, and 3 for environment quality. This framework can then provide a basis for:

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  comparing indicators as a function of specific traffic management strategies,

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  attaching weights to the metrics that reflect planning objectives,

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  stressing the relationship between policy goals and intended city level impact, and

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  monitoring progress towards long term policy goals.

Operationalizing the framework is the key to its usability for cities managers. SAEF provides a set of measurable indicators in context of a broad framework. It provides a mechanism for prioritizing city’s objectives with an understanding of the trade offs that must be made to achieve certain objectives, for example if a traffic management strategy attempts to reduce fuel consumption it may result in more vehicles travelling through neighborhoods. City planners can then determine whether the reduced emissions are higher priority than the likely safety costs of higher traffic flows on neighborhoods streets. They can choose specific themes from the framework based on their own objectives and values and evaluate them in context of proposed traffic management strategies.

Each indicator is detailed in Appendix -A, including a description, the unit of measurement and spatial/temporal levels. The indicators are structured in spatial and temporal dimensions. The spatial dimension is partitioned into individual, community, and city levels (Figure 3). There are 2 temporal dimensions - peak hours or entire day - based on the indicator relevance. The list of indicators is not intended to be exhaustive. It can be revised as new information becomes accessible.

The next section addresses how to quantify the indicators. Once the indicators are quantified, the decision makers can weight them to generate an aggregate score for decision making. The weighting should reflect the goals of the city and rationalize the values of the indicators. Visualization of the indicators in context of various strategies is an alternative method.

In order to evaluate impacts of various routing strategies, we use the results from an agent based simulator Mobiliti [6]. Mobiliti is an urban-scale simulation platform for evaluating network traffic dynamics. Mobiliti provides three optimization methods based on standard traffic assignment algorithms. Specifically, it implements a Quasi-Dynamic Traffic Assignment (QDTA) [7]. The main components of the simulator are detailed in Figure 4. For a given demand, route choice is generated in the QDTA step based on the principles of user equilibrium or system optimal. The optimization objectives are travel time or fuel (refer Appendix -B for the specific objective functions). The network loading is generated using the Mobiliti network simulation, with trips paths assigned by the optimization step. The transportation network for Bay area has over 1 million links and 0.5 million nodes. The trip demand is defined by a travel demand model which for the Bay Area is the SFCTA CHAMP 6 model accounting for $\sim$19 million trips during a 24-hour period [29].

We studied three optimization scenarios: (1) user equilibrium travel time (UET), (2) system optimal travel time (SOT), and (3) system optimal fuel (SOF). The typical optimization for trip level shortest travel time (UET) is compared against the system optimal strategies. The embedded complexity of transportation networks naturally requires trade-offs when optimizing for different objectives. Our goal with the framework is to provide context for understanding the trade-offs. The results are examined for four cities: San Jose, San Francisco, Oakland and Concord. These cities were selected to represent different-sized conurbations, relevant to their population and geographical area. Figure 5 shows all Bay area cities with the chosen four case study cities highlighted.

Flow distribution on roads will differ based on how the system is optimized. Hence understanding the distribution of traffic flows on road types is relevant for impact elevation. Traditionally, road classification systems are based on mobility and access for vehicular use [30]. This study seeks to incorporate the local context of streets, which includes how the dynamics of the vehicular traffic impacts localized populations. To this end, we created a road classification scheme based on the principles of USDOT complete street guidelines [31]. Complete streets provides guidelines for the design and operation of streets to enable safe use and support mobility of all citizens. Adoption of these guidelines is underway by a variety of cities. For example, San Francisco has classified their street based on land use characteristics, transportation roles and special characteristics (eg. alleys, parkways). This resulted in 16 classes that were designed using extensive community surveys and manual labelling [32]. Similarly, San Jose classified streets into 8 types based on a street’s primary function and adjacent land use context [33]. Because we wish to compare our indicators across all of Bay Area cities, we created an alternative classification scheme based on parcel level zoning data and street functional classes. This classification scheme, of 8 types, allowed us to then partition and develop improved metrics that will be helpful for evaluating the themes of our framework.

Our classification uses the Mobiliti road network, which was derived from a professional map from HERE Technologies [34]. The HERE technologies map includes definitions of five functional class roads defined in Appendix -C Table 2. For identifying the transport context, links are classified into three types - highways, throughways, and neighborhood streets. These are based on the functional classification and speeds: highways group higher functional class links of 1, 2, and 3 with speeds greater than 50 mph, throughways group rest of class 3 and 4 links that carry greater volumes and higher speeds of vehicle traffic, and neighborhood streets group class 5 links with lower speeds and volumes. For identifying land use context, parcel level zoning data obtained from Metropolitan Transportation Commission (MTC) is used. There are 1,956,207 parcels in the analysis region grouped into either of the five land uses - residential, commercial, industrial, public-semi public and others. For each link, the side use is determined based on the zoning of the adjacent parcels. If the link is associated with more than one parcel, the use of the largest parcel is assigned to the link. Based on the transport and land use context for each link, 8 street types are established. This classification composition for Bay Area streets is shown in Figure 6. Appendix -C Table 3 details the network lengths associated with the classification. The classification, while slightly coarser than a more detailed partitioning, does not involve the complexity of surveys and manual labeling and can still provide a good understanding of the network typologies. Figure 7 shows the resulting context based link classification for the four cities of interest.

Quantifying the indicators described in the framework can be accomplished by using existing models, developing new models, and accessing city data, eg. zoning use, numbers of accidents, traffic flow and speeds from highway and city detectors, locations of schools. For our initial approach, we chose to confine our attention to the development of the broad framework as discussed in the previous section and examine a subset of indicators. A list of indicators evaluated for our case study cities and their corresponding method for evaluation is provided in Table II. The Mobiliti simulation results for each optimization scenario described in the previous section provided link level flow and speeds for our evaluations.

For each city, fifteen indicators from the SAEF framework are calculated and compiled using a stacked bar chart for visual comparison across the routing optimization scenarios. Figure 9 presents the results for San Jose. Summary table and charts for the three other cities are provided in the Appendix -E. For added insights, we compare optimization scenarios for each of the indicators, similarly we also compare across our four focus cities. For the rest of this section we will discuss results for San Jose. For comparison of optimizations, we take UET optimized routes as our baseline.

where:

$c_{a}(f_{a})=c_{0,a}(1+\alpha(\tfrac{f_{a}}{C_{a}})^{\beta})$
$c_{a}(f_{a})$ is the travel time on link $a$; $f_{a}$ is the traffic flow assigned to link $a$; $c_{0,a}$ and $C_{a}$ are the free-flow travel time and capacity associated with the link; $\alpha$ and $\beta$ selected are BPR parameters 0.15 and 4 respectively.

Bay Area network functional classification is provided in Table 2 and the network typologies are provided in Table 3

The safety performance function parameters estimated from PEMS data for the state of California is provided in Table 4 [40].

Summary of results is provided in Table 5 for the case study cities. Figure13 shows the SAEF charts for the three cities.