Hangzhou Binjiang District Big Data + Comprehensive Governance of All-Region Transportation Project

A Demonstration Model for Comprehensive Urban Transportation Governance in Megacities, Integrating Big Data with All‑Domain Traffic Management

Case Analysis

Hangzhou High-Tech Industrial Development Zone (Binjiang) serves as both a hub and a demonstration zone for Zhejiang Province’s digital‑industry strategy. It administers three subdistricts and 62 communities, with a permanent population of approximately 700,000, covering a total area of about 73 square kilometers. The district boasts a strong concentration of emerging industries, high land‑use intensity, and a highly developed economy, ranking first in the province in per capita GDP. As the only administrative district in Hangzhou’s main urban area that does not impose vehicle‑restriction measures, Binjiang experiences significantly higher motorized‑vehicle travel volumes during peak periods than other districts. In 2019, its peak‑hour congestion index ranked second lowest among Hangzhou’s central districts, making it one of the most severely traffic‑congested areas in the city.

Problem Analysis

The Binjiang District’s transportation system faces four major challenges:

(1) The road network structure is suboptimal, with traffic concentrated on the main arterial roads. Due to insufficient foresight in the early planning of Binjiang District regarding the pace of socio-economic development, congestion has become increasingly severe in recent years. In 2019, the peak-hour congestion index for Binjiang District reached 1.61, with peak values frequently exceeding 2.0 (indicating severe congestion). The district’s road network suffers from a “two‑short, one‑excessive” imbalance: few secondary streets, limited inter‑district connectors, and an abundance of dead‑end roads.

(2) Public transportation infrastructure is inadequate, resulting in a low modal share for commuting. As a hub for the digital industry, Binjiang features high land-development intensity, with industrial‑zone floor area ratios exceeding 3.0—more than twice that of other central urban districts. However, the corresponding public‑transport services fall far short of demand, manifesting in insufficient route coverage and numerous service gaps, an uneven distribution characterized by dense north‑side networks and sparse south‑side ones, as well as inadequate feeder connections that diminish overall attractiveness.

(3) Severe job–residence imbalance and high demand for motorized travel. First, the job–residence imbalance is pronounced, with a clear pendulum‑type commuting pattern. In Binjiang, 73% of commuters cross district boundaries during peak hours, while intra‑district commuters account for less than 30%, indicating a severe misalignment between workplaces and residences. Second, commuting distances are long, driving substantial demand for motorized travel.

(4) Insufficient refined management results in low resource utilization. First, road resources are not utilized with sufficient precision. In Binjiang District, many roads lack fine‑grained traffic channelization, leading to underutilized intersection capacity and, during peak periods, severe congestion on specific approaches at certain intersections. Second, signal timing lacks refinement. Due to an inadequate perception‑based infrastructure, signal timing remains largely single‑point and fixed‑cycle, with limited regional coordination and adaptive control, resulting in a relatively low overall level of intelligence. Third, parking resources are underutilized. Although Binjiang District has 222,000 parking spaces—maintaining a high provision rate—the absence of sensing capabilities and shared‑use applications hampers the efficient mobilization and allocation of parking resources across the area.

Solution

(1) Governance Approach
Qianfang Technology played a pivotal role in the project’s development, serving as both the lead designer and the overall implementer. Grounded in big‑data analytics and centered on the dual pillars of “refinement and intelligence,” Qianfang Technology adopted an integrated, multi‑pronged approach—optimizing road network structure, traffic management, transportation engineering, public transit, non‑motorized mobility, parking systems, and technology‑driven control—and earned the client’s full endorsement.

In response to the actual traffic conditions in Binjiang, Qianfang Technology has innovatively developed a “Big Data + Comprehensive Urban Traffic Governance” framework tailored to key areas. By addressing issues at every stage—“identifying the root causes,” “prescribing targeted solutions,” and “evaluating effectiveness”—the company has successfully implemented governance initiatives in demonstration zones such as the Provincial Children’s Hospital, the Second Affiliated Hospital of Zhejiang University, the Tianjie commercial district, and the Internet Industry Park, thereby conducting meaningful exploratory practices toward more refined urban traffic management.

(II) Governance Methods
At the macro level, leveraging big data serves as the foundation for comprehensive urban traffic governance. By emphasizing the enabling role of big data throughout the pre‑, mid‑, and post‑implementation phases, this approach advances refined traffic‑management initiatives and intelligent traffic‑control systems. Seven key integrated measures—optimizing road network structure, refining traffic organization, enhancing traffic engineering, improving parking management, promoting non‑motorized mobility, upgrading public‑transport systems, and strengthening technology‑driven control—are employed to address diverse scenarios, with particular focus on schools, hospitals, commercial districts, industrial parks, and transportation hubs.

At the micro level, Qianfang Technology has introduced a targeted “1+X” governance framework, deploying a comprehensive set of measures to address traffic congestion. In this “1+X” model, the “1” represents the primary challenge in any given scenario, while the “X” comprises multiple strategies designed to tackle that core issue, thereby yielding tailored solutions for diverse settings such as schools, hospitals, and industrial parks.

Customer Value

1. In the first quarter of 2021, the region’s peak-hour congestion index decreased by 5% year-on-year.

2. The peak-hour delay index around the Binjiang Campus of Zhejiang University Children’s Hospital decreased by 25.8%, and following the implementation of traffic management measures, the monthly average number of patients seen at both Zhejiang University Children’s Hospital and the Binjiang Campus of the Second Affiliated Hospital of Zhejiang University increased by 120,000.

3. In the Longhu Tianjie commercial district, the average daily duration of congestion decreased by 44.2%, and following the implementation of traffic management measures, monthly foot traffic increased by 30,000 visitors.

4. Bus ridership around the Internet Industrial Park increased by 13.7%.

5. The school has added 57 “Qiu Zhi” dedicated bus routes in its vicinity, serving over 6,000 students per day on average and thereby indirectly supporting the school’s “double reduction” policy.