Impact of Transit Quality on Anchor-Mediated Knowledge Economies Across Large U.S. Cities

Universities, medical centers, and headquarters are the driving forces behind cities’ innovation productivity and anchor-based urban revitalization efforts, such as innovation districts. As a result, there is increasing competition for corporate anchors, as well as emerging partnerships and conflicts. In each case, transit has a major role to play. Yet we know little about precisely how transit fits into the dynamics of anchor-based revitalization. To address this empirical gap, this study employed structural equation modeling to grasp both the direct and indirect impacts of transit on the knowledge-intensive firm location in 500 large U.S. cities. We conceptualized the indirect impact of transit as mediated by anchors with a composite value that we developed to quantitatively represent the presence and size of major innovation anchors in each city. According to our findings, the positive impact of transit on a city’s overall knowledge economy occurred through its role in supporting anchor institutions, a role that, in turn, significantly increased the likelihood of knowledge-based firms locating in the city. In short, transit quality was positively associated with larger and more established anchors, as well as the availability of more employees, which expands the city’s talent pool, a critical driver of knowledge-intensive employers’ location decisions. These findings call for greater attention to be paid to transit in cities’ anchor-based urban revitalization plans and to partnerships between cities, metropolitan organizations, and anchors in planning future transit systems.

Modified Presplit Blast Design and its Implementation to Control Near Field Blast Vibrations

The development of urban infrastructure projects like metro rail projects in the major cities in India is one of the challenging tasks due to several site construction and operating constraints. These rapid transit systems (RTS) are conceived to minimise traffic congestion by providing commuters with fast and efficient transportation alternatives. One such project is the Phase-II of the ongoing Bangalore Underground Metrorail Project. The design and construction of the metro rail project require sound engineering judgment and field experience on envisaged strata conditions along the proposed route alignment. The important factors that govern the excavation cycle depend on Rock mass material properties, efficient blast design and construction performance. All these considerations needs to be evaluated for achieving safe, cost-effective excavation design layouts. Proper blast design and safe blasting operations play a key role in achieving good fragmentation, minimising over break and equipment downtime. Site-specific innovative methods on controlled blasting techniques are being experimented with and demonstrated to minimise the ground vibrations. The major challenge lies with the design of efficient and smooth wall blasting techniques to safeguard the old heritage buildings and other subsurface structures and utilities.

Enabling In-Depot Automated Routing and Recharging Scheduling for Automated Electric Bus Transit Systems

The bus transit system is promising to enable electric and autonomous vehicles for massive urban mobility, which relies on high-level automation and efficient resource management. Besides the on-road automation, the in-depot automated scheduling for battery recharging has not been adequately studied yet. This paper presents an integrated in-depot routing and recharging scheduling (IDRRS) problem, which is modeled as a constraint programming (CP) problem with Boolean satisfiability conditions (SAT). The model is converted to a flexible job-shop problem (FJSP) and is feasible to be solved by a CP-SAT solver for the optimal solution or feasible solutions with acceptable performance. This paper also presents a case study in Shanghai and compares the results from the FJSP model and the first-come first-serve (FCFS) method. The result demonstrates the allocation of routes and chargers under multiple scenarios with different numbers of chargers. The results show that the FJSP model shortens the delay and increases the time conservation for future rounds of operation than FCFS, while FCFS presents the simplicity of programming and better computational efficiency. The multiple random input test suggests that the proposed approach can decide the minimum number of chargers for stochastic charging requests. The proposed method can conserve the investment by increasing the utilization of automated recharging devices, improving vehicles’ in-depot efficiency.

Identifying Subway Passenger Flow under Large-Scale Events Using Symbolic Aggregate Approximation Algorithm

Various social events, such as holidays, important sporting events, and major celebrations, may result in sudden large-scale passenger flows in certain sections and stations of urban rail transit systems. The sudden inbound passenger flows caused by these events can easily lead to continuous congestion of the subway network, which has a profound impact on the safety, reliability, and stability of a subway system. Because of the large magnitude of swipe data and the high dimensionality of time series, it is difficult to identify the emergence of such large passenger flows. Additionally, the recognition accuracy of the existing identification methods cannot meet the operational monitoring requirements. To address the above-mentioned issues, this paper proposes an optimized symbolic aggregate approximation (SAX) algorithm to identify historical sudden passenger flows caused by large-scale events around subways. Specifically, pre-set cluster types and dynamic time warping (DTW) are proposed to enhance the matching rate. Compared with the K-means method, the proposed method exhibits an average increase of 30% in mining accuracy, and the calculation time is shortened to one-sixteenth of the original value.