Chapter 2
Review of Related Literature
This section discusses studies related to our research objectives. Section 2.1 dis- cusses route recommendation techniques, followed by Section 2.2 which tackles passenger comfort as it relates to the road as well as systems designed to mea- sure it, while Section 2.3 discusses road safety focused on lighting. Lastly, we synthesize these studies and discuss how it relates to our objectives in Section 2.4.
2.1 Route Recommendations
A study of routing navigation services by Ceikute and Jensen in 2013 found con- trasts in drivers who are familiar with the area (i.e., local drivers) with the routes recommended by these route recommendation services. In their research, they found that by using the travel histories of local drivers significantly increase the quality of existing route recommendations. Not only that travel time can be more accurately be predicted, but users are recommended routes that local drivers take.
Usually, local drivers hold better information about routes, and these may not necessarily be the fastest or shortest.
There are several studies that validate this idea that by simply recommending routes that are optimized for cost or travel time, modern navigation systems are not able to produce the best routes and are also not able to meet other prefer- ences that users might have. In some cases, a better driving experience is preferred (Pfleging et al., 2014) or a longer route can be substituted with a happier route (Quercia et al., 2014). Sometimes, a more familiar route is also preferred by drivers, and they also might seek local information in order to augment their nav- igation (Peeta & Yu, 2004; Samson & Sumi, 2019). Standard routing algorithms
such as A* or Dijstrak’s algorithm can be extended with custom cost functions in order to incorporate other metrics in route generation. Because of increasing complexity in user preferences and factors to take into account when generating routes, techniques that are more efficient and more advanced have been developed on top of these.
User preferences are typically expressed in linguistical form (e.g., “I prefer safer routes”, “Paved over unpaved roads”, “Route should be less than 30 minutes”).
Because of this nature, rule-based techniques have been proposed, particularly using fuzzy logic and inference. These techniques make it easier to model user preferences and classify whether or not routes satisfy these preferences. The work of Mokhtari et al. (2009) details a formal query language (see Figure 2.1) that supports user preferences and describes a method for executing these queries on a fuzzy set-based approach to route recommendation. Fuzzy set-based approaches can also be adapted to support hybrid models of both quantitative and qualita- tive factors. With a hybrid model, the work of Peeta and Yu (2004) is able to incorporate changes in day-to-day and within-day dynamics in driver route choice behavior. Users are able to select pre-trip preferences for route recommendations, and as updated traffic information is provided, the model is able to generate more recommendations as the driver is en-route.
Figure 2.1: Sample route query Mokhtari et al. (2009)
Another approach exploits past travel trajectories to recommend routes to users. This method of recommending routes is particularly useful for users who are not familiar with an area as it relies on the typical behavior of local or past drivers. Chen et al. (2011); Cui et al. (2018) proposed a “popular route” discovery technique that produces routes that have less intersections or turns. By finding sub-routes that have continuous edges along the most popular routes, they are also able to form optimal routes with respect to their popularity function. Luo et al. (2013) also attempts to locate the most popular paths in trajectory data, but they enhance the technique by allowing time period-specific queries. By finding popular routes given a certain time period, we can also reveal and recommend preferred routes in an area by past drivers given certain conditions such as rush hours or limited-time occasions such as sporting events. In order to support personalized recommendations, Dai et al. (2015) also considers a driver’s arbitrary set of preferences and compares that to those of past drivers in their trajectory
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dataset. The final route suggested by their system satisfies three conditions: past drivers with similar preferences must have traversed similar edges in their graph representation, the edges that are traversed the most are considered, and the route with the shortest path is selected.
A smart navigation proposed by Amirgholy et al. (2017) uses a dynamic mixed logit model that also takes into account information learned from a user’s travels overtime in order to continuously estimate and refine a user’s route choice prefer- ence model. In their study, they presented an approach to estimating the utility of a route by using a linear combination of various factors such as pavement qual- ity, road safety, scenic quality, and toll ways, on top of the usual options such as distance and time factors weighted by their relative importance to the user.
Information about the factors can be mined from various sources and then fed into their technique.
With the rise of connected users and smartphone-based driving navigation applications, information from other users have also been used for route recom- mendations. In Waze, users are able to receive and report information about road conditions. While this is not directly used in their route recommendations, users can act accordingly and change their driving behavior. Zheng et al. (2016) uses a crowdsourced system for recommending routes. In their system, workers are tasked to answer a route query. They propose a worker selection process that selects the best worker to answer a particular query based on their response time, load, and familiarity with the area. In order to reduce the latency of crowd responses, they employ previous verified truths about routes into the route rec- ommendations.
Table 2.1 presents a summary of these related studies on route recommenda- tions. From these studies, we can find a common pattern in how route recommen- dations are performed: first, information about routes can be mined from various sources such as social media, travel history, and traffic databases, then user prefer- ences are learned or modeled via the proposed technique, and lastly, these models are then incorporated into cost functions for path finding algorithms.
Table 2.1: Summary of route recommendation techniques
Study Technique Data Used Description
Peeta and Yu (2004) Hybrid Fuzzy Model Qualitative preferences (such as familiarity, route complexity) and quantitative variables (such as travel distance, toll costs, estimated travel time) and user choices overtime
Route recommendations are constantly updated pre-trip and en-route with technique supporting changes in day-to-day and within-day dynamics
Mokhtari et al. (2009) Fuzzy Logic - Presents a framework for modeling
complex user preferences using a fuzzy set-based system and a query language integrating linguistic expressions Chen et al. (2011) Most popular routes
using Absorbing Markov Chain
Past trajectory data Does not directly use user preferences, rather relies on past behavior of a mass of local drivers to recommend routes.
Luo et al. (2013) Time-sensitive popular routes using Frequency Patterns
Past trajectory data with temporal information
Extends the most popular route technique with the ability to query specific time periods for a time-localized recommendation
Dai et al. (2015) Most popular routes using a Hidden Markov Model
Past trajectory data with user preferences
Considers user preferences of past drivers together with their trajectory data in order to recommend popular routes of users with similar preferences
Amirgholy et al.
(2017)
Dynamic Mixed Logit Model
Stated preference surveys, user’s own historical route choice data, online sources
Considers stated user preferences together with their route choices overtime for recommending routes
Zheng et al. (2016) Crowdsourced route recommendations
Online route recommendation services and crowdsourced recommendations
Employs a worker selection process to find the best person to answer a route recommendation query and establishes verified answers in order to improve latency for future requests
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2.2 Comfort
Passenger comfort in vehicles have long been studied, as it is of vital importance to guarantee a pleasant experience while in the vehicle. Corbridge (1987) presents three categories for factors that affect comfort: dynamic factors such as shocks, vibrations, and acceleration; ambient factors such as noise, temperature, and air;
and spatial factors associated with ergonomics.
Dynamic factors are directly affected by the road itself. The road surface type as well as irregularities found on the road contribute to the shocks and vibrations that passengers might experience while inside the vehicle. Additionally, obstacles found on the road can also cause sudden bursts of acceleration or deceleration, as well as vertical displacement of passengers. Different devices and techniques have been used to measure the road surface irregularities such as aluminum beams, lev- els, lasers, and various kinds of profilometers. Generally speaking, road roughness measurement methods can be divided into three distinct types: contact measure- ment, non-contact measurement, and system response-based estimation (Nguyen et al., 2019).
In order to standardize quantifying the conditions of roads, different rough- ness indices were created. The international roughness index (IRI) is a reference index developed out of the international road roughness experiment (IRRE) and is the most used index worldwide for evaluating and managing pavements. The commonly used units for IRI are millimeters per meter (mm/m) or meters per kilometer (m/km). It is calculated by the accumulation of a vehicle suspension’s motion divided by the distance traveled by the vehicle along the length of a road segment (Sayers et al., 1986).
Measurement of IRI is a time-consuming procedure where trained operators use special equipment such as the GMR Profilometer (Spangler & Kelly, 1966) in a tedious process of repetitive driving at constant speeds. These tools typically produce a waveform representing the road profile where the Y-axis shows height variations in the road (see Figure 2.2). Because of this, development on alter- native measurement methods have been done. For example, Chang et al. (2009) developed an autonomous robot equipped with a laser and a laser rangefinder in order to automate the IRI measurement process and found it comparable to commercial profilometers.
Studies by Lakusic et al. (2011) and Du et al. (2014) use vehicles equipped with accelerometers and GPS devices in order to estimate the IRI. In these studies, they established the connection between the Z-axis acceleration of the vehicle and the wheels and the IRI. On top of the Z-axis acceleration, Du et al. also analyzed
the influence of speed on their models with the use of GPS, and found that adding a speed correction coefficient to their models improved the relative error of their estimated IRI. Similarly, with the rise of smartphones, their internal acceleration and GPS sensors can now be used to achieve the same results with more easily accessible hardware (Islam, 2015; Gamage et al., 2016).
Although not directly measuring road roughness, the use of computer vision to measure road irregularities has also been explored. By analyzing images of road surfaces, it is possible to detect road surface variations such as where cracks occur, as well as bigger irregularities such as potholes or cats-eye. Image process- ing techniques are effortless and safe, and can be performed in a short amount of time. A study by Rajab et al. (2008) compares the performance of image pro- cessing to measure alligator cracks (Figure 2.3) and potholes to that of manual measurement. They found the image measurements are reasonably close to that of manual measurement.
Figure 2.2: Road profile recording (Spangler & Kelly, 1966)
Figure 2.3: Digital measurement of alligator cracks (Rajab et al., 2008)
Systems by Kim and Ryu (2014), and Jo and Ryu (2015) operate on images and video frames in order to detect potholes. In both these studies, the general idea is to convert images into grayscale, perform brightness and contrast adjustments, and detect edges after noise removal or masking in order to detect areas of interest.
These techniques yielded positive results, however, they are also susceptible to variations in lighting and weather, as well as the general surface quality of the roads.
Lastly, there are neural network-based techniques where models are trained using thousands of images in order to detect road surface types. Wang et al. (2011) combines this with an accelerometer-based method in order to detect the road surface type (see Figure 2.4). They found that fusing these two techniques yielded a higher (90%) accuracy as it solves the issues of simply using one or the other
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(67% for vibration profile classification and 88% for image classification). The image classification technique was susceptible to variations in lighting conditions, as well as blur induced by vehicle speed, while the accelerometer-based technique cannot properly classify similar road surface types such as asphalt and coarse concrete as they produce the same vibration profile.
A model trained using U-Net with ResNet-based encoders was created by Rateke et al. (2019) in order to detect the road surface type as well as classify the quality of the road. They created a dataset called the Road Traversal Knowledge (RTK) dataset consisting of 77,000+ images with a mix of rough and normal road conditions as they found that existing road datasets use images from well- developed countries with primarily good conditions. In a follow-up study, they augmented their road surface type detection with semantic segmentation (see Figure 2.5), with the ability to differentiate the classes of various points of interests found in an image, such as cats-eye, road markings, potholes, cracks, and so on.
With their technique, they were able to achieve an average accuracy of 90% for the different labels (Rateke & von Wangenheim, 2021).
Figure 2.4: Terrain classification (Wang et al., 2011)
Figure 2.5: Road surface segmen- tation (Rateke & von Wangenheim, 2021)
The devices and techniques used to measure or estimate road roughness are summarized in Table 2.2.
Table 2.2: Comparison of road roughness measurement methods
Method Related author(s) Advantages Disadvantages Description
Direct measurement (aluminum rods and levels, vehicle-mounted or
trailer-towed devices such as the Longitudinal Profile Analyzer)
Piasco and Legeay (1997); ASTM E1364-95 (2017)
Accurate, simple, straightforward
Inefficient, time-consuming, inconvenient (rods and levels), requires
vehicle-specific equipment
& post-processing (LPA)
Used as baseline for comparison, measurement of height from ground contact point, or vertical displacement of wheels, convertible to IRI (in/mi or m/km)
Indirect measurement (lasers, inertial measurements)
Spangler and Kelly (1966); Chang et al.
(2009)
Accurate, convenient, efficient
Specialized precise equipment, high cost of operation
Outputs road profiles as waveforms or height measurements, convertible to a reference index such as IRI (see Figure 2.2)
System response-based estimation (IMUs, smartphones)
Rajab et al. (2008);
Wang et al. (2011);
Lakusic et al. (2011);
Du et al. (2014); Islam (2015); Gamage et al.
(2016)
Cheap, mass availability
Not precise, requires calibration per vehicle
Measures vertical acceleration (in mg or m/s2), vibrations of vehicles (in mm/s), which is then correlated with IRI Image processing & remote
sensing
Wang et al. (2011);
Kim and Ryu (2014);
Jo and Ryu (2015);
Rateke et al. (2019);
Rateke and von Wangenheim (2021)
Fast, effortless, safe
Just a correlation and not a direct measurement of roughness, requires uniform data for the same
techniques
Measures road irregularity dimensions (see Figure 2.3), classifies terrain types (see Figure 2.4), and segments road types, irregularities, markings, and obstacles (see Figure 2.5)
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2.3 Safety
Komackova and Poliak (2016) describe the causal network that influences road safety as a combination of the demographic structure of the population, the driver, the environment - as infrastructure and traffic conditions, and the vehicle. The interactions of these various components under various conditions such as high speed or physical or cognitive impairment leads to crashes and injuries, which could be minor, severe, or fatal. This network corresponds to the factors outlined by Ringhand (2019). Although the interactions of these different components of the network is important, if we explore the use of safety as a factor from the perspective of route generation, infrastructure and traffic conditions are the components directly connected to the road segments themselves.
When looking at the environment of a road network, there are various factors for both infrastructure and traffic conditions that affect safety. For instance, traffic density and flow fall under traffic conditions, which are affected by various features of the road, such as the its shape and size. Modern navigation systems already take these into account and provide for options to avoid such routes. For example, Waze provides options to avoid highways which typically involve faster speeds, as well as difficult intersections, which they define as intersections that have limited visibility, no traffic lights, a constant flow of traffic. or a combination of all three (Waze, 2016a). Factors that influence comfort can also affect safety.
Obstacles found on the road or surface irregularities such as potholes affect the acceleration and deceleration of vehicles, thus also affecting the flow of traffic.
On top of these, weather can also affect the safety of a road, with water and ice reducing friction and causing a loss of traction leading to accidents.
Another infrastructure component that affects safety is road lighting, which is found to have a more than 30% reduction in crashes in areas where poor lighting is improved (Elvik, 1995; Wanvik, 2009). This component is overlooked in modern navigation systems, which do not have settings to prefer routes that have better overall lighting conditions. As far as environmental factors are concerned, poor lighting conditions during nighttime are found to have the highest impact on the number of fatalities and serious injuries, although it has the least impact on lighter accidents compared to the other factors (Wanvik, 2009; Jackett & Frith, 2013).
In order to support various road management bodies, the International Com- mission on Illumination (CIE) has created several guidelines and recommendations on proper road lighting. As the international authority on light and illumination, they also provide the methods for which to properly calculate the various char- acteristics of road lighting quality. Specifically, in CIE 140:2019, they provide formulas and methods for calculating luminance and illuminance, an example of
which is shown in Figure 2.6 on the proper positioning of calculation points for road luminance. Specific policies and guidelines for each country or area are still determined by local governing bodies. For example, in the Philippines, the Department of Energy publishes all guidelines for road lighting (Department of Energy, 2017).
Figure 2.6: Position of calculation points for luminance (CIE 140:2019, 2019)
Road lighting vendors and management bodies that measure these road light- ing characteristics typically rely on manual measurements with spot meters and specialized applications. In order to speed up these tasks, several studies have been made in order to automate the process or provide alternative measurement tools. Zatari et al. (2005) developed a system that automatically measured glare, luminance, and illuminance of road lighting using vehicle mounted CCD cameras (see Figure 2.7). In order to use CCD cameras for lighting measurement, they found that calibration of these cameras is crucial. Similar measurement methods were used by Ekrias et al. (2008) with varying camera specifications. The gen- eral result is that use of CCD cameras for measuring lighting can produce errors of within 10%. However, despite the significant decrease in measurement and processing time, these approaches require high-cost equipment.
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Figure 2.7: Vehicle mounted CCD and GPS equipment (Zatari et al., 2005)
Hiscocks and Syscomp (2011) performs the measurement of luminance by tak- ing photos using inexpensive consumer-grade cameras. The technique involves converting image data into a brightness value by averaging the color pixel values resulting in a grayscale image. They then establish a direct correlation between the grayscale pixel values and the illuminance values. They also use a 3rd party application called ImageJ to create a luminance map (see Figure 2.8). While the their tests were conducted indoors against LED array fixtures, they provide for- mulas for conversion between pixel values and luminance and illuminance, given known values for various light sources which can then be applied to road scenes.
Figure 2.8: ImageJ software showing luminance map
In a study by Cai and Li (2014), they developed a system to measure both lighting and geometry of roadway data through the use of a consumer-grade cam- era. With the use of high dynamic range (HDR) photogrammetry techniques, they were able to measure luminance of a road scene, as well as XYZ coordinates of millions of points on the road using the same device. Their method significantly decreases time spent measuring lighting data compared to traditional methods us- ing spot meters, as it only requires around 1 to 2 minutes to capture an entire road scene. This is longer compared to using specialized CCD camera that captures both images and luminance mappings in one go, however, it is a more afford- able solution. Their results show an error between 1.5% to 10.1% for luminance measurements.
Another technique that uses aerial imagery instead of street-level images is presented by Rabaza et al. (2018). In the study, they use an innovative method of compilating data from various sources such as geographic information systems (GIS) as well as their own aerial images collected via drones cameras, and present them as layers on top of a map (see Figure 2.9). With their platform, they are able to calculate lighting intensities and uniformity of illuminance of the roads, as
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well as estimate energy efficiency of these lighting installations.
Figure 2.9: Aerial imagery overlaid on top of a map (Rabaza et al., 2018)
The distribution of lighting also plays a role in both the comfort and safety of drivers, as it could lead to visual strain and reduced concentration. Kumar et al.
(2016) presents a mobile sensor platform that monitors urban street lighting in- frastructure. Their eventual goal is to produce a semi-live virtual representation of street lighting, showing the location and performance of street illumination. Using support vector machines (SVM), they are able to classify light sources in an image as either a lamp or non-lamp. Additionally, they provide an algorithm for esti- mating the height of the light source, and the use of light meters mounted on the car top to perform light intensity mapping. With this information, measurement of both road lighting quality, distribution, and uniformity and be calculated.
The studies on measuring road lighting are summarized in Table 2.3.
Table 2.3: Summary of alternative road lighting measurement studies
Technique Author(s) Data Measured Findings
Manual measurement via spot meters
CIE 140:2019 Luminance, illuminance Provides standard formulas and guidelines for measuring road lighting Automatic measurement
using CCD cameras and GPS
Zatari et al. (2005); Ekrias et al. (2008)
Glare, luminance, illuminance
Establishes calibration methods, automated measurements, and found an accuracy of±10% of spot meters Automatic measurement
using consumer-grade cameras
Hiscocks and Syscomp (2011) Luminance, illuminance Presents a method to obtain luminance and illuminance from consumer-grade camera images using known average values for various lighting fixtures
Automatic measurement using HDR techniques
Cai and Li (2014) Luminance, illuminance Uses consumer-grade cameras and multiple long exposure images to capture more information, faster than manual spot meters but slower than non-HDR methods
Automatic measurement using multiple sensors and CCD cameras
Kumar et al. (2016) Luminance, illuminance, road geometric structure, lighting position, fixture classification
Presents a reconstructed virtual representation of road scenes with road and lighting information for lighting infrastructure monitoring Measurement using aerial
imagery and online data
Rabaza et al. Luminance, illuminance,
energy consumption
Compiles information on roads and lighting from GIS and aerial images, presents a visualization tool using map layers, with information on lighting intensify, uniformity, and energy efficiency
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2.4 Synthesis
While there have been various studies and techniques developed to measure the safety and comfort of roads, these have not seen wide use as far as being in- corporated into route recommendations. The most popular driving navigation application with over 1 billion monthly active users - Google Maps (Google, 2020) - does not have an option to choose safer or more comfortable routes. Similarly, Waze - with 65 million monthly active users in 185 countries (Waze, 2016b) only contains an extra option to avoid difficult intersections.
In 2015, Li et al. developed a cloud-aided safety-based route planner that included estimated time and a road risk index (RRI) metric to generate their recommendations. In order to calculate the RRI, they created a hybrid artificial neural network (ANN) model using information mined from road and accident databases as input. The output predicted crash rates in different severity levels, which used is then as part of the cost function of their route planner. Similarly, in 2016, Li et al. developed a cloud-aided comfort-based route planner that es- timated road profiles and detected anomalies, which are then fed as factors into an extended implementation of Dijkstra’s algorithm to generate routes. In both of these studies, case studies on real routes were carried out which validated the accuracy of their safety, comfort, and route recommendations. However, studies on whether drivers actually prefer these routes were not carried out.
Studies such as the one carried out by Samson and Sumi (2019) reveal why it is important to perform real-life driving experiments in order to analyze route choice behavior. While stated preference surveys and driving simulations play important roles, latent preferences of users are not revealed until presented with additional information or changes in context. Additionally, due to the diversity of driver characteristics and preferences, it is important to consider different factors that affect route choice, and not simply relying on the prevalent shortest distance or time techniques for route generation. Tawfik and Rakha (2012) found that driver demographics, personality traits, and choice situation characteristics were found to be significant in modeling driver diversity. They also found that these factors are sometimes as important or more important than trip characteristics such as travel time when it comes to route choice.
Optimizing for a single factor such as distance or time when generating routes is a relatively trivial pursuit given the abundance of techniques for finding the shortest paths between two points. When we start taking into account other factors when generating routes, it becomes more difficult to present not just the best route but also alternative routes. For example, a driver might prefer a safer and more comfortable driving experience route. However, the driver might also
be in a rush. When finding the possible routes between two points, we find that the safer route takes 15 minutes longer than the fastest route, or the comfortable route is not as safe as the faster route. With the findings from Tawfik and Rakha (2012), we want to avoid suggesting routes that are the extremes and aim to provide a more balanced recommendation given driver preferences.
Thus, in order to meet the research objectives we set out, we will be exploring various techniques as well as improve upon techniques outlined in past studies.
Fuzzy logic closely resembles human reasoning and allows us to easily model route choice rules. While past research has been done on the use of it in route genera- tion, these studies have not been validated in real-world driving scenarios and its effect on driver’s route choice behavior has not been analyzed. Additionally, tech- niques in road analysis have been carried out, but primarily to only measure road characteristics. We will be applying these techniques in the context of measuring driving suitability in order to produce better route recommendations.
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