Cities across the globe face unprecedented growth due to urbanization. A large segment of society now live in urban areas [1
], and, by 2050, 66% will reside in cities [2
]. This statistic is concerning given that current urban mobility systems—which are largely dependent on the automobile—are untenable due to the lack of space, environmental externalities, inefficient energy use, and concerns over public health [3
]. Thus, integrating public transit with emission-free transport modes, such as bicycling, remains critical for creating sustainable transportation systems that increase the quality of urban life.
Interest in combining travel modes among policy-makers is growing in light of global urbanization trends, infrastructure resilience fears, urban health concerns, and the need for promoting sustainable transportation systems [4
]. The notion of creating sustainable transportation systems within cities and universities alike has been a goal for at least the last 25 years [8
]. It has been posited that an important first step for creating such a system is to understand the ecology of transport modes. For instance, in many cities, a complex system of travel modes and infrastructures coexist (i.e., driving, transit, bicycle lanes, scooters, etc.), but rarely interact [6
]. This undermines the full potential of creating a sustainable transportation system. Therefore, a better understanding of the relationship among different transportation systems is needed.
In recent decades, researchers and policy-makers have proposed ways to integrate bicycling and transit to ease transit access and egress conditions. The assimilation has several benefits; chief among them is that it enhances transit’s catchment area and adoption [10
]. This is realized, through the combined short-distance travel-sheds and speed afforded by bicycling, with the long-distance travel capabilities of buses, rail, and other transit modes. Additionally, when bicycles are integrated with transit, they can promote livable communities, replace automobile trips, and elevate public health [11
]. Common approaches to promote this integration have consisted of: innovative planning interventions, policy analysis, economic performance measurements, and educational programming [14
]. Furthermore, assimilating bicycling and transit can occur in several ways: (a) ride and park near the transit stop, (b) sharing bicycle use (i.e., bicycle rentals), (c) integrated planning and operation, and (d) bicycles-on-board buses (referred to as “BoB” hereafter) and regulation [19
]. The ability to bring bicycles on-board transit is a popular option among transit riders, transit agencies, and universities [8
]. Approximately 72% of U.S. transit systems have bicycle racks installed on buses, thus making the BoB mode interventions the most popular strategy for increasing “first and last mile” transit access [20
]. The “first and last mile” term has been coined by planners to describe the distances traveled before and after boarding transit [21
]. Despite the popularity of BoB mode interventions in the U.S.—80–90% of transit trips start by either walking or bicycling—the relationship remains complex and poorly understood [15
The viability of intermodal transport using BoB remains mixed. Previous research has shown that these systems are underutilized, delay transit, have limited rack capacity, and increase transit delays [19
]. Research from Europe has indicated that carrying bicycles on board transit during peak times takes up space for others and may make other passengers uncomfortable [24
]. Other studies show that BoB is increasing in use and popularity. Pucher [25
] and Krizek et al. [15
] highlighted a clear demand for BoB mode shares and Ensor et al. [26
] indicated that cycle-transit use may provide substantial economic benefit for transit agencies. In contrast, much like people’s attitudes towards transit, negative sentiments also prevailed among cycle-transit users in a recent study conducted in Philadelphia [27
]. BoB mode shares may also be a primary mobility option for those without low-income persons without automobiles [28
] and university students [8
]. Other related research has shown that the main predictors for this mode are trip distance, weather, and trip purposes [21
]. What remains indeterminate is how personal and neighborhood conditions together may facilitate (or negate) this mode choice during the “first-mile”, especially in a university environment. This leaves us with an incomplete understanding of how to effectively elevate this mode, consequently reducing the full potential for universities, and their host cities, to reach their sustainability goals.
In recognition of this research need, the current study had two research objectives: (i) describe the travel behavior and preferences towards BoB mode choice among a university sample using empirical and exploratory spatial data analysis (ESDA), and (ii) implement a discrete choice modeling strategy, coupled with geovisualizations, to assess the probability of using BoB as a function of spatially varying personal and neighborhood conditions in the City of Flint.
Integrating bicycling and transit—including BoB—is part of an effective TDM toolkit, yet it is an often-overlooked travel consideration in practice and research within the U.S. [60
]. This leaves us with an incomplete understanding of how to effectively promote this mode in cities and universities alike. The present study has added to the existing, albeit minimal, body of work examining BoB mode choice by showing how, and where, significant personal and neighborhood factors affect the probability of choosing BoB for the “first mile” portion of a university-based trip. Furthermore, the empirical analysis demonstrated that most respondents were generally uninterested in BoB; however, a small contingent residing close to the City’s CBD appeared willing to use this mode. The results of our discrete choice modeling strategy highlighted gender, commute time, and bicycle ownership were important personal factors; while park access, bicycle facility access, and land-use diversity, also influenced the probability of choosing this mode. The GWLR coefficient maps displayed the geographical impact of these factors; underscoring the importance of devising localized interventions to induce a mode shift towards BoB travel in a university environment.
Our first research objective (i) was to empirically and spatially describe the travel behaviors of the university sample, with a focus on BoB interest. The descriptive statistics showed that the majority of the sample utilized the automobile for non-university and university travel. We also saw that significant portions of the MCC and KU sample utilized transit and walking modes, respectively. One explanation for the elevated walking mode (max 34.4%) for the KU contingent is that 68.9% of the respondents reported commute times less than 10 min. We found that BoB interest was greatest among the UM-Flint and MCC sample—nearly 40% of the respondents stating “yes” to potentially using BoB. They also reported the longest commute times, high automobile accessibility, and reduced perceived access to bus stops. Thus, targeting groups from these two universities may have the largest impact on BoB mode choice. The ESDA (i.e., “hot-spot analysis”) showed where statistically significant clusters of university persons interested in BoB resided (see Figure 2
c). We found that those living close to UM-Flint and the CBD were most interested in BoB, which is in part supported by past research showing interest in intermodal transportation is highest in urban areas [62
]. Interestingly, despite slight interest in BoB from KU respondents, the university is close to groups of university constituents willing to use BoB. The result indicates that the attitudes towards BoB transportation are nuanced and may be attributed to university typology or local neighborhood conditions. Nonetheless, it highlights that context-sensitive planning and policy interventions are needed. Although some past research has shown that BoB mode shares are popular in the U.S. and a strong component of many university TDM strategies [8
], we found the prospect of using this mode among our sample low, apart from a cluster near the City’s CBD.
The second objective (ii) of this research was to implement a discrete choice modeling framework—using two global models and one spatial model—to explain how much, and where, personal factors and neighborhood conditions affect BoB interest. Models 2 (GLR) and 3 (GWLR) were the strongest in terms of fit statistics; however, their explanatory power was modest (max R2
= 0.400). Nonetheless, we discovered several personal factors were associated with BoB interest. Males and bicycle owners favored BoB; the association was strongest in the City’s southwest neighborhoods. This finding finds support from previous works showing that males are more inclined to combine bicycling and transit [63
] and tend to bicycle more than females [64
]. Unsurprisingly, we found that bicycle ownership was also important when considering BoB modes; the finding builds on similar past works [65
]. Combining bicycling and transit can be effective for medium or long commutes [66
]; however, our results differ in that we found that the likelihood of using BoB diminished significantly for respondents living in the far northeast and southwest sections of the City, where commute times were greater than 30 min (>3 km from the City’s CBD). Due to the scarcity of bus routes in these areas (see Figure 1
), and the importance of time for intermodal transport users [62
], it isn’t unreasonable to infer that respondents may be opting to use the bicycle for their commute—because of its efficiency for long-distance travel [67
], or the automobile. A logical intervention, therefore, would be to increase the level of transit service in periphery neighborhoods and examine bicycle facility access to ensure that these two modes can efficiently assimilate to promote BoB mode shares when traveling to the university.
Several neighborhood conditions were also found to affect probabilities of BoB mode choice. A noteworthy finding was the relationship between two accessibility indicators-parks and bicycle facilities—and BoB interest. The probability of using BoB modes decreased when respondents had limited access (i.e., increased distance) to parks: the northern half of the city exhibited the strongest relationship. Previous works highlighting the effect of park density on predicting intermodal transportation demonstrated a similar relationship [68
]. Our results may also be an artifact of the known links between neighborhood quality, greenspace, and activity levels of the resident population [69
]. In other words, the observed relationship found in this study may in part be linked to active residents self-selecting neighborhoods with elevated greenspace density, and perhaps inclined to choose active travel modes, such as BoB. Similar to past research speculating that bicycle network density has a minimal impact on bicycling rates to train stations [70
], we discovered that overall neighborhood bikeability (i.e., BLOS) also showed no effect; rather, distance to a bicycle facility mattered. Therefore, a place-based strategy involving the installation of bicycle facilities (i.e., separated bicycle lanes, repair stations, and wayfinding signage) in areas that are safe and in high demand may elevate BoB interest for the “first-mile” of university travel. Our finding that land-use diversity had a positive impact on BoB interest resonates with previous research on bicycle transit integration [68
]. We found that land-use diversity may persuade individuals to use this mode throughout the City, especially in neighborhoods northeast of the CBD (see Figure 3
e). Areas in the southeast section showed a weaker relationship, highlighting that interventions may be needed there. To encourage BoB interest in these neighborhoods, it is suggested that the bus level of service be examined, and the MTA continues to work with the City to expand its bike-share network into these areas to ease bicycle–transit integration.